dataset,canonical_name,name_source,author_year,n_subjects,n_records,n_tasks,modality of exp,type of exp,Type Subject,duration_hours_total,size_bytes,size,source,dataset_title,record_modality,nchans_set,sampling_freqs,license,doi,nemar_citation_count
EEG2025r1,"[""HBN_r1_bdf""]",canonical,Shirazi2024_R1_bdf,136,1342,10,Visual,Clinical/Intervention,Development,,22141722527.0,20.6 GB,nemar,Healthy Brain Network (HBN) EEG - Release 1 (BDF Converted),eeg,"[{""val"": 129, ""count"": 1342}]","[{""val"": 100.0, ""count"": 1342}]",CC-BY-SA 4.0,10.18112/openneuro.ds005505.v1.0.1,
EEG2025r10,"[""HBN_r10_bdf""]",canonical,Shirazi2025_R10_bdf,533,2516,8,Visual,Clinical/Intervention,Development,,34477112576.0,32.1 GB,nemar,Healthy Brain Network (HBN) EEG - Release 10 (BDF Converted),eeg,"[{""val"": 129, ""count"": 2516}]","[{""val"": 100.0, ""count"": 2516}]",CC-BY-SA 4.0,,
EEG2025r10mini,"[""HBN_r10_bdf_mini""]",canonical,Shirazi2025_R10_bdf_mini,20,220,8,Visual,Clinical/Intervention,Development,,3014691878.0,2.8 GB,nemar,Healthy Brain Network (HBN) EEG - Release 10 (BDF Converted),eeg,"[{""val"": 129, ""count"": 220}]","[{""val"": 100.0, ""count"": 220}]",CC-BY-SA 4.0,,
EEG2025r11,"[""HBN_r11_bdf""]",canonical,Shirazi2025_R11_bdf,430,3397,8,Visual,Clinical/Intervention,Development,,47062121390.0,43.8 GB,nemar,Healthy Brain Network (HBN) EEG - Release 11 (BDF Converted),eeg,"[{""val"": 129, ""count"": 3397}]","[{""val"": 100.0, ""count"": 3397}]",CC-BY-SA 4.0,,
EEG2025r11mini,"[""HBN_r11_bdf_mini""]",canonical,Shirazi2025_R11_bdf_mini,20,220,8,Visual,Clinical/Intervention,Development,,3047885400.0,2.8 GB,nemar,Healthy Brain Network (HBN) EEG - Release 11 (BDF Converted),eeg,"[{""val"": 129, ""count"": 220}]","[{""val"": 100.0, ""count"": 220}]",CC-BY-SA 4.0,,
EEG2025r1mini,"[""HBN_r1_bdf_mini""]",canonical,Shirazi2024_R1_bdf_mini,20,239,10,Visual,Clinical/Intervention,Development,,3943272491.0,3.7 GB,nemar,Healthy Brain Network (HBN) EEG - Release 1 (BDF Converted),eeg,"[{""val"": 129, ""count"": 239}]","[{""val"": 100.0, ""count"": 239}]",CC-BY-SA 4.0,10.18112/openneuro.ds005505.v1.0.1,
EEG2025r2,"[""HBN_r2_bdf""]",canonical,Shirazi2024_R2_bdf,150,1405,10,Visual,Clinical/Intervention,Development,,24025278407.0,22.4 GB,nemar,Healthy Brain Network (HBN) EEG - Release 2 (BDF Converted),eeg,"[{""val"": 129, ""count"": 1405}]","[{""val"": 100.0, ""count"": 1405}]",CC-BY-SA 4.0,10.18112/openneuro.ds005506.v1.0.1,
EEG2025r2mini,"[""HBN_r2_bdf_mini""]",canonical,Shirazi2024_R2_bdf_mini,20,240,10,Visual,Clinical/Intervention,Development,,4103962147.0,3.8 GB,nemar,Healthy Brain Network (HBN) EEG - Release 2 (BDF Converted),eeg,"[{""val"": 129, ""count"": 240}]","[{""val"": 100.0, ""count"": 240}]",CC-BY-SA 4.0,10.18112/openneuro.ds005506.v1.0.1,
EEG2025r3,"[""HBN_r3_bdf""]",canonical,Shirazi2024_R3_bdf,184,1812,10,Visual,Clinical/Intervention,Development,,29929328615.0,27.9 GB,nemar,Healthy Brain Network (HBN) EEG - Release 3 (BDF Converted),eeg,"[{""val"": 129, ""count"": 1812}]","[{""val"": 100.0, ""count"": 1812}]",CC-BY-SA 4.0,10.18112/openneuro.ds005507.v1.0.1,
EEG2025r3mini,"[""HBN_r3_bdf_mini""]",canonical,Shirazi2024_R3_bdf_mini,20,240,10,Visual,Clinical/Intervention,Development,,3964149485.0,3.7 GB,nemar,Healthy Brain Network (HBN) EEG - Release 3 (BDF Converted),eeg,"[{""val"": 129, ""count"": 240}]","[{""val"": 100.0, ""count"": 240}]",CC-BY-SA 4.0,10.18112/openneuro.ds005507.v1.0.1,
EEG2025r4,"[""HBN_r4_bdf""]",canonical,Shirazi2024_R4_bdf,324,3342,10,Visual,Clinical/Intervention,Development,,49350723331.0,46.0 GB,nemar,Healthy Brain Network (HBN) EEG - Release 4 (BDF Converted),eeg,"[{""val"": 129, ""count"": 3342}]","[{""val"": 100.0, ""count"": 3342}]",CC-BY-SA 4.0,10.18112/openneuro.ds005508.v1.0.1,
EEG2025r4mini,"[""HBN_r4_bdf_mini""]",canonical,Shirazi2024_R4_bdf_mini,20,240,10,Visual,Clinical/Intervention,Development,,3544037582.0,3.3 GB,nemar,Healthy Brain Network (HBN) EEG - Release 4 (BDF Converted),eeg,"[{""val"": 129, ""count"": 240}]","[{""val"": 100.0, ""count"": 240}]",CC-BY-SA 4.0,10.18112/openneuro.ds005508.v1.0.1,
EEG2025r5,"[""HBN_r5_bdf""]",canonical,Shirazi2024_R5_bdf,330,3326,10,Visual,Clinical/Intervention,Development,,48140200409.0,44.8 GB,nemar,Healthy Brain Network (HBN) EEG - Release 5 (BDF Converted),eeg,"[{""val"": 129, ""count"": 3326}]","[{""val"": 100.0, ""count"": 3326}]",CC-BY-SA 4.0,10.18112/openneuro.ds005509.v1.0.1,
EEG2025r5mini,"[""HBN_r5_bdf_mini""]",canonical,Shirazi2024_R5_bdf_mini,20,240,10,Visual,Clinical/Intervention,Development,,3473736650.0,3.2 GB,nemar,Healthy Brain Network (HBN) EEG - Release 5 (BDF Converted),eeg,"[{""val"": 129, ""count"": 240}]","[{""val"": 100.0, ""count"": 240}]",CC-BY-SA 4.0,10.18112/openneuro.ds005509.v1.0.1,
EEG2025r6,"[""HBN_r6_bdf""]",canonical,Shirazi2024_R6_bdf,135,1227,10,Visual,Clinical/Intervention,Development,,19498582229.0,18.2 GB,nemar,Healthy Brain Network (HBN) EEG - Release 6 (BDF Converted),eeg,"[{""val"": 129, ""count"": 1227}]","[{""val"": 100.0, ""count"": 1227}]",CC-BY-SA 4.0,10.18112/openneuro.ds005510.v1.0.1,
EEG2025r6mini,"[""HBN_r6_bdf_mini""]",canonical,Shirazi2024_R6_bdf_mini,20,237,10,Visual,Clinical/Intervention,Development,,3766229819.0,3.5 GB,nemar,Healthy Brain Network (HBN) EEG - Release 6 (BDF Converted),eeg,"[{""val"": 129, ""count"": 237}]","[{""val"": 100.0, ""count"": 237}]",CC-BY-SA 4.0,10.18112/openneuro.ds005510.v1.0.1,
EEG2025r7,"[""HBN_r7_bdf""]",canonical,Shirazi2024_R7_bdf,381,3100,10,Visual,Clinical/Intervention,Development,,0.0,Unknown,nemar,Healthy Brain Network (HBN) EEG - Release 7 (BDF Converted),eeg,"[{""val"": 129, ""count"": 3090}, {""val"": 6, ""count"": 10}]","[{""val"": 100.0, ""count"": 3100}]",CC-BY-SA 4.0,10.18112/openneuro.ds005511.v1.0.1,
EEG2025r7mini,"[""HBN_r7_bdf_mini""]",canonical,Shirazi2024_R7_bdf_mini,20,239,10,Visual,Clinical/Intervention,Development,,0.0,Unknown,nemar,Healthy Brain Network (HBN) EEG - Release 7 (BDF Converted),eeg,"[{""val"": 129, ""count"": 239}]","[{""val"": 100.0, ""count"": 239}]",CC-BY-SA 4.0,10.18112/openneuro.ds005511.v1.0.1,
EEG2025r8,"[""HBN_r8_bdf""]",canonical,Shirazi2024_R8_bdf,257,2320,10,Visual,Clinical/Intervention,Development,,33755682484.0,31.4 GB,nemar,Healthy Brain Network (HBN) EEG - Release 8 (BDF Converted),eeg,"[{""val"": 129, ""count"": 2320}]","[{""val"": 100.0, ""count"": 2320}]",CC-BY-SA 4.0,10.18112/openneuro.ds005512.v1.0.1,
EEG2025r8mini,"[""HBN_r8_bdf_mini""]",canonical,Shirazi2024_R8_bdf_mini,20,238,10,Visual,Clinical/Intervention,Development,,3462867427.0,3.2 GB,nemar,Healthy Brain Network (HBN) EEG - Release 8 (BDF Converted),eeg,"[{""val"": 129, ""count"": 238}]","[{""val"": 100.0, ""count"": 238}]",CC-BY-SA 4.0,10.18112/openneuro.ds005512.v1.0.1,
EEG2025r9,"[""HBN_r9_bdf""]",canonical,Shirazi2024_R9_bdf,295,2885,10,Visual,Clinical/Intervention,Development,,39735523870.0,37.0 GB,nemar,Healthy Brain Network (HBN) EEG - Release 9 (BDF Converted),eeg,"[{""val"": 129, ""count"": 2885}]","[{""val"": 100.0, ""count"": 2885}]",CC-BY-SA 4.0,10.18112/openneuro.ds005514.v1.0.1,
EEG2025r9mini,"[""HBN_r9_bdf_mini""]",canonical,Shirazi2024_R9_bdf_mini,20,237,10,Visual,Clinical/Intervention,Development,,3264235409.0,3.0 GB,nemar,Healthy Brain Network (HBN) EEG - Release 9 (BDF Converted),eeg,"[{""val"": 129, ""count"": 237}]","[{""val"": 100.0, ""count"": 237}]",CC-BY-SA 4.0,10.18112/openneuro.ds005514.v1.0.1,
ds000117,"[""Wakeman2015"", ""WakemanHenson""]",canonical,Wakeman2018,17,104,2,Visual,Perception,Healthy,,94108833435.0,87.6 GB,openneuro,"Multisubject, multimodal face processing",meg,"[{""val"": 394, ""count"": 96}]","[{""val"": 1100.0, ""count"": 96}]",CC0,doi:10.18112/openneuro.ds000117.v1.1.0,77.0
ds000246,[],,Bock2018,2,3,2,Auditory,Perception,Healthy,0.20833333333333334,2457671893.0,2.3 GB,openneuro,MEG-BIDS Brainstorm data sample,meg,"[{""val"": 340, ""count"": 2}, {""val"": 301, ""count"": 1}]","[{""val"": 2400.0, ""count"": 3}]",CC0,doi:10.18112/openneuro.ds000246.v1.0.1,1.0
ds000247,"[""OMEGA""]",canonical,Niso2018,6,10,2,Resting State,Resting-state,Healthy,1.0158333333333334,11024087953.0,10.3 GB,openneuro,MEG-BIDS OMEGA RestingState_sample,meg,"[{""val"": 297, ""count"": 5}, {""val"": 330, ""count"": 3}, {""val"": 300, ""count"": 2}]","[{""val"": 2400.0, ""count"": 10}]",CC0,doi:10.18112/openneuro.ds000247.v1.0.2,3.0
ds000248,"[""MNE_Sample_Data""]",canonical,Gramfort2018,2,3,2,Multisensory,Attention,Healthy,0.10769794469291934,186216741.0,177.6 MB,openneuro,MNE-Sample-Data,meg,"[{""val"": 376, ""count"": 1}, {""val"": 315, ""count"": 1}]","[{""val"": 600.614990234375, ""count"": 2}]",CC0,10.18112/openneuro.ds000248.v1.2.4,3.0
ds001785,[],,Pereira2019_Evidence,18,54,3,,,,25.714475,26701806128.0,24.9 GB,openneuro,Evidence accumulation relates to perceptual consciousness and monitoring,eeg,"[{""val"": 71, ""count"": 54}]","[{""val"": 1024.0, ""count"": 53}, {""val"": 1000.0, ""count"": 1}]",CC0,10.18112/openneuro.ds001785.v1.1.1,2.0
ds001787,[],,Delorme2019,24,40,1,,,,27.607222222222223,6112012191.0,5.7 GB,openneuro,EEG meditation study,eeg,"[{""val"": 79, ""count"": 40}]","[{""val"": 256.0, ""count"": 40}]",CC0,doi:10.18112/openneuro.ds001787.v1.1.1,6.0
ds001810,[],,Reteig2019,47,263,1,,,,91.205,49117378796.0,45.7 GB,openneuro,"EEG study of the attentional blink; before, during, and after transcranial Direct Current Stimulation (tDCS)",eeg,"[{""val"": 73, ""count"": 263}]","[{""val"": 512.0, ""count"": 263}]",CC0,10.18112/openneuro.ds001810.v1.1.0,6.0
ds001849,[],,Freedberg2019,20,120,1,,,,,47790430570.0,44.5 GB,openneuro,RS_TMSEEG_Data,eeg,"[{""val"": 30, ""count"": 120}]","[{""val"": 5000.0, ""count"": 120}]",CC0,10.18112/openneuro.ds001849.v1.0.2,1.0
ds001971,[],,Wagner2019,20,273,1,,,,39.845172526041665,34339201028.0,32.0 GB,openneuro,Audiocue walking study,eeg,"[{""val"": 115, ""count"": 206}, {""val"": 112, ""count"": 67}]","[{""val"": 512.0, ""count"": 273}]",Creative commons,10.18112/openneuro.ds001971.v1.1.1,2.0
ds002001,"[""Mendola2020""]",author_year,Mendola2019,11,69,2,Visual,Perception,Healthy,,87694131395.0,81.7 GB,openneuro,Rivalry_Tagging,meg,[],"[{""val"": 2400, ""count"": 69}]",PD,10.18112/openneuro.ds002001.v1.0.0,3.0
ds002034,[],,Schneider2019,14,167,4,,,,35.84861111111111,10842685064.0,10.1 GB,openneuro,Real-time EEG feedback on alpha power lateralization leads to behavioral improvements in a covert attention task,eeg,"[{""val"": 81, ""count"": 167}]","[{""val"": 512.0, ""count"": 167}]",CC0,doi:10.18112/openneuro.ds002034.v1.0.3,7.0
ds002094,[],,DS2094_Single_pulse,20,43,3,Other,Clinical/Intervention,Unknown,19.601310944444446,42339775606.0,39.4 GB,openneuro,Single-pulse open-loop TMS-EEG dataset,eeg,"[{""val"": 30, ""count"": 43}]","[{""val"": 5000.0, ""count"": 43}]",CC0,,30.0
ds002158,[],,Pereira2019_Disentangling,20,117,1,,,,17.080816666666667,82185122527.0,76.5 GB,openneuro,Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging,eeg,"[{""val"": 64, ""count"": 117}]","[{""val"": 5000.0, ""count"": 117}]",CC0,10.18112/openneuro.ds002158.v1.0.2,1.0
ds002181,[],,Xie2019,226,226,1,Visual,Resting-state,Development,7.675835,158221569.0,150.9 MB,openneuro,CRYPTO and PROVIDE EEG Baseline Data,eeg,"[{""val"": 125, ""count"": 226}]","[{""val"": 500.0, ""count"": 226}]",CC0,mockDOI,1.0
ds002218,[],,Comstock2019,18,18,1,,,,16.52023003472222,2089143297.0,1.9 GB,openneuro,Auditory and Visual Rhythm Omission EEG,eeg,"[{""val"": 32, ""count"": 18}]","[{""val"": 256.0, ""count"": 18}]",CC0,mockDOI,1.0
ds002312,"[""OcularLDT"", ""ocular_ldt""]",canonical,Brooks2019,19,23,1,Visual,Perception,Healthy,7.096010277777777,36600686939.0,34.1 GB,openneuro,OcularLDT,meg,"[{""val"": 257, ""count"": 23}]","[{""val"": 1000.0, ""count"": 15}]",CC0,10.18112/openneuro.ds002312.v1.0.0,
ds002336,[],,Lioi2019_multi,10,54,6,,,,11.043072222222222,18046628207.0,16.8 GB,openneuro,A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP1,eeg,"[{""val"": 64, ""count"": 54}]","[{""val"": 5000.0, ""count"": 54}]",CC0,10.18112/openneuro.ds002336.v2.0.2,4.0
ds002338,[],,Lioi2019_multi_modal,17,85,4,,,,15.745905555555556,26024158019.0,24.2 GB,openneuro,A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and fMRI acquisition during a motor imagery neurofeedback task: XP2,eeg,"[{""val"": 64, ""count"": 85}]","[{""val"": 5000.0, ""count"": 85}]",CC0,10.18112/openneuro.ds002338.v2.0.1,11.0
ds002550,[],,Quentin2020,22,377,2,Visual,Memory,Healthy,30.509356643518515,179848999649.0,167.5 GB,openneuro,Differential brain mechanisms of selection and maintenance of information during working memory (MEG data),meg,"[{""val"": 308, ""count"": 367}, {""val"": 307, ""count"": 8}, {""val"": 304, ""count"": 2}]","[{""val"": 1200.0, ""count"": 374}, {""val"": 12000.0, ""count"": 3}]",CC0,10.18112/openneuro.ds002550.v1.0.1,0.0
ds002578,[],,Delorme2020_Visual_Oddball_256,2,2,1,,,,1.455,1429242343.0,1.3 GB,openneuro,Visual Oddball Task (256 channels),eeg,"[{""val"": 256, ""count"": 2}]","[{""val"": 256.0, ""count"": 2}]",CC0,10.18112/openneuro.ds002578.v1.1.0,1.0
ds002680,[],,Delorme2020_Go_nogo_categorization,14,350,1,,,,20.808333333333334,9902131689.0,9.2 GB,openneuro,Go-nogo categorization and detection task,eeg,"[{""val"": 31, ""count"": 350}]","[{""val"": 1000.0, ""count"": 350}]",CC0,10.18112/openneuro.ds002680.v1.2.0,5.0
ds002691,[],,Delorme2020_Internal_attention,20,20,1,,,,6.721111111111111,814480634.0,776.7 MB,openneuro,Internal attention study,eeg,"[{""val"": 32, ""count"": 20}]","[{""val"": 250.0, ""count"": 20}]",CC0,10.18112/openneuro.ds002691.v1.1.0,2.0
ds002712,[],,Aurtenetxe2020,25,82,1,Visual,Perception,Healthy,24.06388888888889,109278657976.0,101.8 GB,openneuro,Numbers and Letters,meg,"[{""val"": 312, ""count"": 79}, {""val"": 361, ""count"": 2}, {""val"": 314, ""count"": 1}]","[{""val"": 1000.0, ""count"": 82}]",CC0,10.18112/openneuro.ds002712.v1.0.1,1.0
ds002718,"[""Wakeman2015"", ""WakemanHenson_EEG_MEG""]",canonical,Wakeman2020,18,18,1,,,,14.844166666666666,4623904945.0,4.3 GB,openneuro,Face processing EEG dataset for EEGLAB,eeg,"[{""val"": 74, ""count"": 18}]","[{""val"": 250.0, ""count"": 18}]",CC0,doi:10.18112/openneuro.ds002718.v1.1.0,11.0
ds002720,[],,Daly2020_recorded,18,165,0,,,,18.774722222222223,2563670728.0,2.4 GB,openneuro,A  dataset  recorded  during  development  of a  tempo-based  brain-computer  music  interface,eeg,"[{""val"": 19, ""count"": 165}]","[{""val"": 1000.0, ""count"": 165}]",CC0,10.18112/openneuro.ds002720.v1.0.1,1.0
ds002721,[],,Daly2020_recorded_affective,31,185,0,,,,26.282777777777778,3598850279.0,3.4 GB,openneuro,An EEG dataset recorded during affective music listening,eeg,"[{""val"": 19, ""count"": 185}]","[{""val"": 1000.0, ""count"": 185}]",CC0,10.18112/openneuro.ds002721.v1.0.2,10.0
ds002722,[],,Daly2020_recorded_development,19,94,0,,,,22.889444444444443,6545791966.0,6.1 GB,openneuro,A dataset recorded during development of an affective brain-computer music interface: calibration session,eeg,"[{""val"": 37, ""count"": 94}]","[{""val"": 1000.0, ""count"": 94}]",CC0,10.18112/openneuro.ds002722.v1.0.1,2.0
ds002723,[],,Daly2020_session,8,44,0,,,,10.46888888888889,2789434877.0,2.6 GB,openneuro,A dataset recorded during development of an affective brain-computer music interface: testing session,eeg,"[{""val"": 37, ""count"": 44}]","[{""val"": 1000.0, ""count"": 44}]",CC0,10.18112/openneuro.ds002723.v1.1.0,1.0
ds002724,[],,Daly2020_sessions,10,96,0,,,,28.610555555555557,9147071936.0,8.5 GB,openneuro,A dataset recorded during development of an affective brain-computer music interface: training sessions,eeg,"[{""val"": 37, ""count"": 96}]","[{""val"": 1000.0, ""count"": 96}]",CC0,10.18112/openneuro.ds002724.v1.0.1,1.0
ds002725,[],,Daly2020_joint,21,105,5,,,,22.538611111111113,16445696685.0,15.3 GB,openneuro,A dataset recording joint EEG-fMRI during affective music listening,eeg,"[{""val"": 46, ""count"": 105}]","[{""val"": 1000.0, ""count"": 105}]",CC0,10.18112/openneuro.ds002725.v1.0.0,2.0
ds002761,[],,Wimmer2020,25,249,2,Visual,Memory,Healthy,,1758242.0,1.7 MB,openneuro,memoryreplay,meg,"[{""val"": 306, ""count"": 249}]","[{""val"": 600.0, ""count"": 249}]",CC0,doi:10.18112/openneuro.ds002761.v1.1.2,1.0
ds002778,[],,Rockhill2020,31,46,1,,,,2.5177528211805558,571470906.0,545.0 MB,openneuro,UC San Diego Resting State EEG Data from Patients with Parkinson's Disease,eeg,"[{""val"": 41, ""count"": 46}]","[{""val"": 512.0, ""count"": 46}]",CC0,doi:10.18112/openneuro.ds002778.v1.0.5,42.0
ds002791,"[""Mheich2020""]",author_year,Mheich2020_DataSet1,23,92,0,Unknown,Unknown,Healthy,13.535969444444444,50562457196.0,47.1 GB,openneuro,DataSet1,eeg,"[{""val"": 256, ""count"": 80}, {""val"": 257, ""count"": 12}]","[{""val"": 1000.0, ""count"": 92}]",CC0,10.18112/openneuro.ds002791.v1.0.0,0.0
ds002799,[],,Thompson2024,27,16824,2,Other,Clinical/Intervention,Epilepsy,,19940287452.0,18.6 GB,openneuro,Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI,ieeg,"[{""val"": 2, ""count"": 79}, {""val"": 4, ""count"": 1}]",[],CC0,10.18112/openneuro.ds002799.v1.0.4,
ds002814,[],,Ebrahiminia2020,21,168,1,,,,,29779119341.0,27.7 GB,openneuro,A Multimodal Neuroimaging Dataset to Study Spatiotemporal Dynamics of Visual Processing in Humans,eeg,"[{""val"": 72, ""count"": 168}]","[{""val"": 1200.0, ""count"": 168}]",CC0,doi:10.18112/openneuro.ds002814.v1.3.0,4.0
ds002833,"[""Mheich2024""]",author_year,Mheich2020_DataSet2,20,80,1,Visual,Other,Healthy,11.603826666666667,42698181625.0,39.8 GB,openneuro,DataSet2,eeg,"[{""val"": 257, ""count"": 80}]","[{""val"": 1000.0, ""count"": 80}]",CC0,10.18112/openneuro.ds002833.v1.0.0,0.0
ds002885,[],,Kandemir2020,2,7,4,Other,Other,Other,0.39958333333333335,21543178912.0,20.1 GB,openneuro,DBS Phantom Recordings,meg,"[{""val"": 306, ""count"": 4}, {""val"": 314, ""count"": 3}]","[{""val"": 19200.0, ""count"": 4}, {""val"": 3000.0, ""count"": 3}]",CC0,10.18112/openneuro.ds002885.v1.0.1,1.0
ds002893,[],,Westerfield2022,49,52,1,,,,37.7789260473112,8262777491.0,7.7 GB,openneuro,Auditory-Visual Shift Study,eeg,"[{""val"": 36, ""count"": 52}]","[{""val"": 250.0, ""count"": 42}, {""val"": 250.0293378038558, ""count"": 10}]",CC0,doi:10.18112/openneuro.ds002893.v2.0.0,1.0
ds002908,"[""Bogacz2024""]",author_year,Bogacz2020,13,53,1,Unknown,Attention,Unknown,5.105833333333333,64199908375.0,59.8 GB,openneuro,Human MEG recordings during sequential conflict task,meg,"[{""val"": 299, ""count"": 53}]","[{""val"": 2400.0, ""count"": 53}]",CC0,10.18112/openneuro.ds002908.v1.0.0,1.0
ds003004,[],,Onton2020,34,34,1,,,,49.07222222222222,38704522994.0,36.0 GB,openneuro,Imagined Emotion Study,eeg,"[{""val"": 219, ""count"": 3}, {""val"": 224, ""count"": 3}, {""val"": 212, ""count"": 2}, {""val"": 221, ""count"": 2}, {""val"": 214, ""count"": 2}, {""val"": 134, ""count"": 1}, {""val"": 209, ""count"": 1}, {""val"": 218, ""count"": 1}, {""val"": 215, ""count"": 1}, {""val"": 235, ""count"": 1}, {""val"": 201, ""count"": 1}, {""val"": 211, ""count"": 1}, {""val"": 189, ""count"": 1}, {""val"": 208, ""count"": 1}, {""val"": 229, ""count"": 1}, {""val"": 220, ""count"": 1}, {""val"": 232, ""count"": 1}, {""val"": 222, ""count"": 1}, {""val"": 231, ""count"": 1}, {""val"": 226, ""count"": 1}, {""val"": 207, ""count"": 1}, {""val"": 206, ""count"": 1}, {""val"": 213, ""count"": 1}, {""val"": 180, ""count"": 1}, {""val"": 196, ""count"": 1}, {""val"": 223, ""count"": 1}, {""val"": 227, ""count"": 1}]","[{""val"": 256.0, ""count"": 34}]",CC0,doi:10.18112/openneuro.ds003004.v1.1.1,7.0
ds003029,[],,Li2020,35,106,1,Other,Clinical/Intervention,Epilepsy,8.208814574268422,11083752416.0,10.3 GB,openneuro,Epilepsy-iEEG-Multicenter-Dataset,ieeg,"[{""val"": 129, ""count"": 30}, {""val"": 132, ""count"": 8}, {""val"": 135, ""count"": 6}, {""val"": 88, ""count"": 6}, {""val"": 147, ""count"": 6}, {""val"": 123, ""count"": 6}, {""val"": 101, ""count"": 5}, {""val"": 91, ""count"": 4}, {""val"": 98, ""count"": 4}, {""val"": 110, ""count"": 3}, {""val"": 53, ""count"": 3}, {""val"": 81, ""count"": 3}, {""val"": 99, ""count"": 3}, {""val"": 60, ""count"": 3}, {""val"": 111, ""count"": 3}, {""val"": 80, ""count"": 3}, {""val"": 89, ""count"": 3}, {""val"": 86, ""count"": 3}, {""val"": 65, ""count"": 2}, {""val"": 216, ""count"": 1}, {""val"": 47, ""count"": 1}]","[{""val"": 1000.0, ""count"": 56}, {""val"": 999.4121105232217, ""count"": 13}, {""val"": 249.85355222464145, ""count"": 10}, {""val"": 999.9999999999999, ""count"": 9}, {""val"": 499.7071044492829, ""count"": 7}, {""val"": 1000.0000000000001, ""count"": 7}, {""val"": 2000.0000000000002, ""count"": 3}, {""val"": 1024.5997950800408, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds003029.v1.0.5,19.0
ds003039,[],,Jacobsen2020,19,19,1,,,,17.507142777777776,8401128487.0,7.8 GB,openneuro,free walking study,eeg,"[{""val"": 67, ""count"": 19}]","[{""val"": 500.0, ""count"": 19}]",CC0,doi:10.18112/openneuro.ds003039.v1.0.2,2.0
ds003061,"[""Delorme""]",canonical,Delorme2020_auditory_oddball,13,39,1,Auditory,Attention,Healthy,8.206666666666667,2421802195.0,2.3 GB,openneuro,EEG data from an auditory oddball task,eeg,"[{""val"": 79, ""count"": 39}]","[{""val"": 256.0, ""count"": 39}]",CC0,doi:10.18112/openneuro.ds003061.v1.1.0,4.0
ds003078,[],,DOMENECH2020,6,72,1,Unknown,Unknown,Surgery,,11761361020.0,11.0 GB,openneuro,PROBE iEEG,ieeg,"[{""val"": 130, ""count"": 1}]",[],CC0,10.18112/openneuro.ds003078.v1.0.0,1.0
ds003082,"[""Cote2015""]",author_year,Cote2020,2,3,2,Auditory,Perception,Healthy,0.29555555555555557,14215257908.0,13.2 GB,openneuro,Auditory Cortex Mapping Dataset,meg,"[{""val"": 300, ""count"": 2}, {""val"": 306, ""count"": 1}]","[{""val"": 12000.0, ""count"": 2}, {""val"": 2400.0, ""count"": 1}]",CC0,10.18112/openneuro.ds003082.v1.0.0,5.0
ds003104,"[""MNESomato"", ""Somato"", ""MNESomatoData""]",canonical,Parkkonen2020,1,1,1,Tactile,Perception,Healthy,0.2491881047669284,349944649.0,333.7 MB,openneuro,MNE-somato-data-bids (anonymized),meg,"[{""val"": 316, ""count"": 1}]","[{""val"": 300.3074951171875, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds003104.v1.0.1,0.0
ds003190,[],,MendozaMontoya2020,19,384,2,Visual,Attention,Healthy,39.74703125,1079425010.0,1.0 GB,openneuro,Assesment of the visual stimuli properties in P300 paradigm,eeg,"[{""val"": 9, ""count"": 382}, {""val"": 10, ""count"": 2}]","[{""val"": 256.0, ""count"": 384}]",CC0,10.18112/openneuro.ds003190.v1.0.1,4.0
ds003194,[],,Vega2020_Neuroepo,15,29,2,,,,7.177747222222222,198333437.0,189.1 MB,openneuro,Neuroepo multisession,eeg,"[{""val"": 19, ""count"": 24}, {""val"": 20, ""count"": 4}, {""val"": 21, ""count"": 1}]","[{""val"": 200.0, ""count"": 29}]",CC0,10.18112/openneuro.ds003194.v1.0.3,3.0
ds003195,[],,Vega2020_Placebo,10,20,2,,,,4.653659722222222,126957087.0,121.1 MB,openneuro,Placebo Neuroepo multisession,eeg,"[{""val"": 19, ""count"": 20}]","[{""val"": 200.0, ""count"": 20}]",CC0,10.18112/openneuro.ds003195.v1.0.3,3.0
ds003343,[],,Schneider2020,20,59,1,,,,6.434444444444445,695587039.0,663.4 MB,openneuro,Disentangling the percepts of illusory movement and sensory stimulation during tendon vibration in the EEG,eeg,"[{""val"": 20, ""count"": 59}]","[{""val"": 500.0, ""count"": 59}]",CC0,10.18112/openneuro.ds003343.v2.0.1,1.0
ds003352,"[""Hermann2021""]",author_year,Hermann2020,18,138,1,Visual,Perception,Healthy,52.308295,230092820068.0,214.3 GB,openneuro,1 - Light Pink Spiral,meg,"[{""val"": 323, ""count"": 138}]","[{""val"": 1000.0, ""count"": 138}]",CC0,10.18112/openneuro.ds003352.v1.0.0,4.0
ds003374,[],,Fedele2020,9,18,1,Visual,Affect,Epilepsy,2.61,175461388.0,167.3 MB,openneuro,Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation,ieeg,"[{""val"": 4, ""count"": 10}, {""val"": 2, ""count"": 8}]","[{""val"": 2000.0, ""count"": 18}]",CC0,10.18112/openneuro.ds003374.v1.1.1,4.0
ds003380,[],author_year,Frasch2020,1,5,0,Anesthesia,Clinical/Intervention,Other,0.08944444444444444,20614209.0,19.7 MB,openneuro,"Corticothalamic communication under analgesia, sedation and gradual ischemia: a multimodal model of controlled gradual cerebral ischemia in pig",eeg,"[{""val"": 16, ""count"": 1}]",[],CC0,10.18112/openneuro.ds003380.v1.0.0,
ds003392,[],,Zilber2020,12,33,2,Visual,Perception,Healthy,1.2324969444444447,10818491467.0,10.1 GB,openneuro,NeuroSpin hMT+ Localizer DATA (MEG & aMRI),meg,"[{""val"": 320, ""count"": 22}]","[{""val"": 2000.0, ""count"": 22}]",CC0,10.18112/openneuro.ds003392.v1.0.4,0.0
ds003420,[],,Mheich2020_HD,23,92,0,Visual,Other,Healthy,13.535969444444444,50565140405.0,47.1 GB,openneuro,HD-EEGtask(Dataset 1),eeg,"[{""val"": 256, ""count"": 80}, {""val"": 257, ""count"": 12}]","[{""val"": 1000.0, ""count"": 92}]",CC0,10.18112/openneuro.ds003420.v1.0.2,1.0
ds003421,[],,Mheich2020_HD_EEGtask,20,80,1,,,,11.603826666666667,42498451545.0,39.6 GB,openneuro,HD-EEGtask(Dataset 2),eeg,"[{""val"": 257, ""count"": 80}]","[{""val"": 1000.0, ""count"": 80}]",CC0,10.18112/openneuro.ds003421.v1.0.2,3.0
ds003458,[],,Cavanagh2021_Three,23,23,1,,,,10.447388888888888,5065148524.0,4.7 GB,openneuro,EEG: Three armed bandit gambling task,eeg,"[{""val"": 66, ""count"": 19}, {""val"": 64, ""count"": 4}]","[{""val"": 500.0, ""count"": 23}]",CC0,10.18112/openneuro.ds003458.v1.1.0,0.0
ds003474,[],,Cavanagh2021_Probabilistic,122,122,1,,,,36.61049388888889,17867698098.0,16.6 GB,openneuro,EEG: Probabilistic Selection and Depression,eeg,"[{""val"": 66, ""count"": 72}, {""val"": 67, ""count"": 50}]","[{""val"": 500.0, ""count"": 122}]",CC0,10.18112/openneuro.ds003474.v1.1.0,9.0
ds003478,[],,Cavanagh2021_Depression,122,243,1,,,,23.35965722222222,11430503904.0,10.6 GB,openneuro,EEG: Depression rest,eeg,"[{""val"": 66, ""count"": 133}, {""val"": 67, ""count"": 110}]","[{""val"": 500.0, ""count"": 243}]",CC0,10.18112/openneuro.ds003478.v1.1.0,22.0
ds003483,"[""Maestu2021""]",author_year,Cognitive2021,21,41,2,Unknown,Decision-making,Healthy,11.018888888888888,26285775768.0,24.5 GB,openneuro,Logical reasoning study,meg,"[{""val"": 320, ""count"": 41}]","[{""val"": 1000.0, ""count"": 41}]",CC0,10.18112/openneuro.ds003483.v1.0.2,3.0
ds003490,[],,Cavanagh2021_3,50,75,1,,,,12.759916666666665,6276604033.0,5.8 GB,openneuro,EEG: 3-Stim Auditory Oddball and Rest in Parkinson's,eeg,"[{""val"": 67, ""count"": 75}]","[{""val"": 500.0, ""count"": 75}]",CC0,10.18112/openneuro.ds003490.v1.1.0,13.0
ds003498,[],,Fedele2021,20,385,0,Resting State,Clinical/Intervention,Epilepsy,32.344113194444446,48030875385.0,44.7 GB,openneuro,interictal iEEG during slow-wave sleep with HFO markings,ieeg,"[{""val"": 64, ""count"": 146}, {""val"": 40, ""count"": 73}, {""val"": 42, ""count"": 43}, {""val"": 74, ""count"": 35}, {""val"": 16, ""count"": 29}, {""val"": 50, ""count"": 28}, {""val"": 52, ""count"": 13}, {""val"": 48, ""count"": 13}, {""val"": 30, ""count"": 5}]","[{""val"": 2000.0, ""count"": 385}]",CC0,10.18112/openneuro.ds003498.v1.0.1,3.0
ds003505,"[""VEPCON""]",canonical,Pascucci2021,19,37,2,,,,,31105987628.0,29.0 GB,openneuro,VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes,eeg,"[{""val"": 128, ""count"": 37}]","[{""val"": 2048.0, ""count"": 37}]",CC0,doi:10.18112/openneuro.ds003505.v1.1.1,5.0
ds003506,[],,Cavanagh2021_Reinforcement,56,84,1,,,,35.38127777777778,17399670602.0,16.2 GB,openneuro,EEG: Reinforcement Learning in Parkinson's,eeg,"[{""val"": 67, ""count"": 84}]","[{""val"": 500.0, ""count"": 84}]",CC0,10.18112/openneuro.ds003506.v1.1.0,4.0
ds003509,[],,Cavanagh2021_Simon,56,84,1,,,,48.53486111111111,23987498608.0,22.3 GB,openneuro,EEG: Simon Conflict in Parkinson's,eeg,"[{""val"": 67, ""count"": 84}]","[{""val"": 500.0, ""count"": 84}]",CC0,10.18112/openneuro.ds003509.v1.1.0,5.0
ds003516,[],,Holtze2021,25,25,1,,,,22.56954166666667,8152002419.0,7.6 GB,openneuro,EEG: Attended Speaker Paradigm (Own Name in Ignored Stream),eeg,"[{""val"": 49, ""count"": 25}]","[{""val"": 500.0, ""count"": 25}]",CC0,10.18112/openneuro.ds003516.v1.1.1,3.0
ds003517,[],,Cavanagh2021_Continuous,17,34,1,,,,13.00626388888889,6262665570.0,5.8 GB,openneuro,EEG: Continuous gameplay of an 8-bit style video game,eeg,"[{""val"": 65, ""count"": 34}]","[{""val"": 500.0, ""count"": 34}]",CC0,10.18112/openneuro.ds003517.v1.1.0,5.0
ds003518,[],,Cavanagh2021_Simon_Conflict,110,137,1,,,,89.88768666666667,42423396129.0,39.5 GB,openneuro,EEG: Simon Conflict w/ Reinforcement + Cabergoline Challenge,eeg,"[{""val"": 64, ""count"": 137}]","[{""val"": 500.0, ""count"": 137}]",CC0,10.18112/openneuro.ds003518.v1.1.0,0.0
ds003519,[],,Cavanagh2021_Visual,27,54,1,,,,20.50396111111111,9623063888.0,9.0 GB,openneuro,EEG: Visual Working Memory + Cabergoline Challenge,eeg,"[{""val"": 64, ""count"": 54}]","[{""val"": 500.0, ""count"": 54}]",CC0,10.18112/openneuro.ds003519.v1.1.0,3.0
ds003522,[],,Cavanagh2021_Three_Stim,96,200,1,,,,57.07904611111111,27224783220.0,25.4 GB,openneuro,EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI,eeg,"[{""val"": 65, ""count"": 192}, {""val"": 64, ""count"": 8}]","[{""val"": 500.0, ""count"": 200}]",CC0,10.18112/openneuro.ds003522.v1.1.0,5.0
ds003523,[],,Cavanagh2021_Visual_Working,91,221,1,,,,84.58588777777777,40304005269.0,37.5 GB,openneuro,EEG: Visual Working Memory in Acute TBI,eeg,"[{""val"": 65, ""count"": 216}, {""val"": 64, ""count"": 5}]","[{""val"": 500.0, ""count"": 221}]",CC0,10.18112/openneuro.ds003523.v1.1.0,3.0
ds003555,[],,Cserpan2021,30,30,1,,,,107.92361111111111,16177352613.0,15.1 GB,openneuro,Dataset of EEG recordings of pediatric patients with epilepsy based on the 10-20 system,eeg,"[{""val"": 23, ""count"": 27}, {""val"": 24, ""count"": 3}]","[{""val"": 1024.0, ""count"": 30}]",CC0,10.18112/openneuro.ds003555.v1.0.1,8.0
ds003568,[],,Liuzzi2021,51,118,2,Visual,Affect,Healthy,22.541666666666668,132520000612.0,123.4 GB,openneuro,Mood induction in MDD and healthy adolescents,meg,"[{""val"": 340, ""count"": 48}, {""val"": 339, ""count"": 29}, {""val"": 335, ""count"": 11}, {""val"": 336, ""count"": 6}, {""val"": 342, ""count"": 5}, {""val"": 343, ""count"": 5}, {""val"": 309, ""count"": 3}, {""val"": 338, ""count"": 3}, {""val"": 312, ""count"": 3}, {""val"": 305, ""count"": 2}, {""val"": 348, ""count"": 1}, {""val"": 310, ""count"": 1}, {""val"": 313, ""count"": 1}]","[{""val"": 1200.0, ""count"": 118}]",CC0,10.18112/openneuro.ds003568.v1.0.2,4.0
ds003570,[],,Goldman2021,40,40,1,,,,26.208055555555557,51133422324.0,47.6 GB,openneuro,EEG: Improvisation and Musical Structures,eeg,"[{""val"": 64, ""count"": 40}]","[{""val"": 2048.0, ""count"": 40}]",CC0,10.18112/openneuro.ds003570.v1.0.0,1.0
ds003574,[],,Sterpenich2021,18,18,1,,,,30.093788888888888,19277929546.0,18.0 GB,openneuro,Reward biases spontaneous neural reactivation during sleep,eeg,"[{""val"": 69, ""count"": 18}]","[{""val"": 500.0, ""count"": 18}]",CC0,10.18112/openneuro.ds003574.v1.0.2,1.0
ds003602,[],,Korucuoglu2021,118,699,6,,,,152.53169944444446,78609742101.0,73.2 GB,openneuro,Childhood Sexual Abuse and problem drinking in women: Neurobehavioral mechanisms,eeg,"[{""val"": 35, ""count"": 699}]","[{""val"": 1000.0, ""count"": 699}]",CC0,10.18112/openneuro.ds003602.v1.0.0,0.0
ds003620,"[""Runabout""]",canonical,Liebherr2021,44,100,1,Auditory,Attention,Healthy,59.53691277777778,18299512036.0,17.0 GB,openneuro,Runabout: A mobile EEG study of auditory oddball processing in laboratory and real-world conditions,eeg,"[{""val"": 35, ""count"": 100}]","[{""val"": 500.0, ""count"": 100}]",CC0,doi:10.18112/openneuro.ds003620.v1.1.1,4.0
ds003626,[],,Nieto2021,10,30,1,,,,12.951944444444445,19624686464.0,18.3 GB,openneuro,Inner Speech,eeg,"[{""val"": 137, ""count"": 1}]",[],CC0,10.18112/openneuro.ds003626.v2.0.0,6.0
ds003633,"[""ForrestGump_MEG""]",canonical,Liu2021,12,96,2,Multisensory,Perception,Healthy,21.97826087962963,78926559646.0,73.5 GB,openneuro,ForrestGump-MEG,meg,"[{""val"": 409, ""count"": 89}, {""val"": 378, ""count"": 7}]","[{""val"": 600.0, ""count"": 89}, {""val"": 1200.0, ""count"": 7}]",CC0,doi:10.18112/openneuro.ds003633.v1.0.3,1.0
ds003638,[],,Cavanagh2021_Electrophysiological,57,57,1,,,,40.5975,16397669261.0,15.3 GB,openneuro,"EEG: Electrophysiological biomarkers of behavioral dimensions 
from cross-species paradigms",eeg,"[{""val"": 72, ""count"": 57}]","[{""val"": 512.0, ""count"": 57}]",CC0,10.18112/openneuro.ds003638.v1.0.0,1.0
ds003645,[],,Wakeman2021,19,224,2,,,,,114157254734.0,106.3 GB,openneuro,Face processing MEEG dataset with HED annotation,"eeg, meg","[{""val"": 404, ""count"": 120}, {""val"": 394, ""count"": 96}]","[{""val"": 1100.0, ""count"": 216}]",CC0,doi:10.18112/openneuro.ds003645.v2.0.2,3.0
ds003655,[],,Pavlov2021_VerbalWorkingMemory,156,156,1,,,,130.92305555555555,21756907235.0,20.3 GB,openneuro,VerbalWorkingMemory,eeg,"[{""val"": 21, ""count"": 156}]","[{""val"": 500.0, ""count"": 156}]",CC0,10.18112/openneuro.ds003655.v1.0.0,4.0
ds003670,[],author_year,Gebodh2021,25,62,1,Visual,Clinical/Intervention,Healthy,72.77206638888889,77549594582.0,72.2 GB,openneuro,"Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS",eeg,"[{""val"": 35, ""count"": 62}]","[{""val"": 2000.0, ""count"": 62}]",CC0,10.18112/openneuro.ds003670.v1.1.0,6.0
ds003682,[],author_year,Wise2021,28,336,1,Unknown,Learning,Healthy,31.7550625,227225155192.0,211.6 GB,openneuro,Model-based aversive learning in humans is supported by preferential task state reactivation,meg,"[{""val"": 414, ""count"": 336}]","[{""val"": 1200.0, ""count"": 336}]",CC0,10.18112/openneuro.ds003682.v1.0.0,1.0
ds003688,[],,Berezutskaya2021,51,107,2,Multisensory,Perception,Epilepsy,9.20130295247396,16334721863.0,15.2 GB,openneuro,Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film,ieeg,"[{""val"": 74, ""count"": 6}, {""val"": 109, ""count"": 5}, {""val"": 62, ""count"": 5}, {""val"": 125, ""count"": 4}, {""val"": 85, ""count"": 4}, {""val"": 107, ""count"": 4}, {""val"": 88, ""count"": 4}, {""val"": 72, ""count"": 4}, {""val"": 67, ""count"": 3}, {""val"": 95, ""count"": 3}, {""val"": 115, ""count"": 3}, {""val"": 71, ""count"": 3}, {""val"": 111, ""count"": 3}, {""val"": 81, ""count"": 3}, {""val"": 118, ""count"": 3}, {""val"": 76, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 102, ""count"": 2}, {""val"": 87, ""count"": 2}, {""val"": 126, ""count"": 2}, {""val"": 80, ""count"": 2}, {""val"": 75, ""count"": 2}, {""val"": 86, ""count"": 2}, {""val"": 122, ""count"": 2}, {""val"": 116, ""count"": 2}, {""val"": 121, ""count"": 2}, {""val"": 112, ""count"": 2}, {""val"": 100, ""count"": 2}, {""val"": 54, ""count"": 2}, {""val"": 177, ""count"": 2}, {""val"": 64, ""count"": 2}, {""val"": 128, ""count"": 2}, {""val"": 89, ""count"": 2}, {""val"": 113, ""count"": 2}, {""val"": 110, ""count"": 2}, {""val"": 60, ""count"": 2}, {""val"": 97, ""count"": 1}, {""val"": 69, ""count"": 1}, {""val"": 65, ""count"": 1}, {""val"": 94, ""count"": 1}, {""val"": 91, ""count"": 1}, {""val"": 92, ""count"": 1}]","[{""val"": 512.0, ""count"": 71}, {""val"": 2048.0, ""count"": 32}, {""val"": 2000.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds003688.v1.0.7,9.0
ds003690,[],,Ribeiro2021,75,375,3,,,,47.65696944444444,23043388712.0,21.5 GB,openneuro,"EEG, ECG and pupil data from young and older adults: rest and auditory cued reaction time tasks",eeg,"[{""val"": 66, ""count"": 365}, {""val"": 64, ""count"": 10}]","[{""val"": 500.0, ""count"": 375}]",CC0,10.18112/openneuro.ds003690.v1.0.0,5.0
ds003694,"[""MEGMEM""]",canonical,Griffiths2021,28,132,1,Unknown,Memory,Unknown,,234581975924.0,218.5 GB,openneuro,MEGMEM,meg,"[{""val"": 327, ""count"": 109}, {""val"": 319, ""count"": 19}, {""val"": 336, ""count"": 4}]","[{""val"": 1000.0, ""count"": 132}]",CC0,10.18112/openneuro.ds003694.v1.0.0,1.0
ds003702,[],,Gregory2021,47,47,1,,,,40.459041666666664,18784103504.0,17.5 GB,openneuro,Social Memory cuing,eeg,"[{""val"": 59, ""count"": 47}]","[{""val"": 500.0, ""count"": 47}]",CC0,10.18112/openneuro.ds003702.v1.0.1,3.0
ds003703,"[""Kalenkovich2019""]",author_year,Kalenkovich2021,34,102,2,Auditory,Perception,Healthy,21.81608277777778,99158117602.0,92.3 GB,openneuro,Frequency Tagging of Syntactic Structure or Lexical Properties,meg,"[{""val"": 314, ""count"": 102}]","[{""val"": 1000.0, ""count"": 102}]",CC0,10.18112/openneuro.ds003703.v1.0.0,1.0
ds003708,"[""Miller2021""]",author_year,Hermes2021,1,1,1,Other,Clinical/Intervention,Unknown,1.1047416178385416,650274380.0,620.1 MB,openneuro,Basis profile curve identification to understand electrical stimulation effects in human brain networks,ieeg,"[{""val"": 89, ""count"": 1}]","[{""val"": 2048.0, ""count"": 1}]",CC0,10.18112/openneuro.ds003708.v1.0.0,1.0
ds003710,"[""APPLESEED""]",canonical,Williams2021,13,48,1,,,,9.1654915,10934705875.0,10.2 GB,openneuro,APPLESEED Example Dataset,eeg,"[{""val"": 32, ""count"": 48}]","[{""val"": 5000.0, ""count"": 48}]",CC0,doi:10.18112/openneuro.ds003710.v1.0.2,1.0
ds003739,[],,Peterson2021_Perturbed_beam_walking,30,120,4,,,,20.57443576388889,11742611120.0,10.9 GB,openneuro,Perturbed beam-walking task,eeg,"[{""val"": 149, ""count"": 120}]","[{""val"": 256.0, ""count"": 120}]",CC0,10.18112/openneuro.ds003739.v1.0.2,5.0
ds003751,"[""DENS""]",canonical,Mishra2021,38,38,1,,,,19.94988888888889,5057922854.0,4.7 GB,openneuro,Dataset on Emotion with Naturalistic Stimuli (DENS),eeg,"[{""val"": 131, ""count"": 38}]","[{""val"": 250.0, ""count"": 38}]",CC0,10.18112/openneuro.ds003751.v1.0.2,7.0
ds003753,[],,Brown2021_Probabilistic,25,25,1,,,,10.10425,4965119239.0,4.6 GB,openneuro,EEG: Probabilistic Learning with Affective Feedback: Exp #2,eeg,"[{""val"": 66, ""count"": 25}]","[{""val"": 500.0, ""count"": 25}]",CC0,10.18112/openneuro.ds003753.v1.1.0,0.0
ds003766,[],,Chen2021,31,124,4,,,,40.77816472222222,76565985366.0,71.3 GB,openneuro,A resource for assessing dynamic binary choices in the adult brain using EEG and mouse-tracking,eeg,"[{""val"": 129, ""count"": 124}]","[{""val"": 1000.0, ""count"": 124}]",CC0,doi:10.18112/openneuro.ds003766.v2.0.3,1.0
ds003768,[],,Gu2021,33,255,2,,,,60.597566666666665,92938507671.0,86.6 GB,openneuro,Simultaneous EEG and fMRI signals during sleep from humans,eeg,"[{""val"": 32, ""count"": 255}]","[{""val"": 5000.0, ""count"": 255}]",CC0,10.18112/openneuro.ds003768.v1.0.0,21.0
ds003774,"[""MUSING""]",canonical,Miyapuram2021,20,240,1,Auditory,Affect,Healthy,8.63974111111111,10863711359.0,10.1 GB,openneuro,Music Listening- Genre EEG dataset (MUSIN-G),eeg,"[{""val"": 129, ""count"": 240}]","[{""val"": 1000.0, ""count"": 132}, {""val"": 250.0, ""count"": 108}]",CC0,10.18112/openneuro.ds003774.v1.0.0,8.0
ds003775,[],,HatlestadHall2021,111,153,1,Resting State,Resting-state,Healthy,10.2,4815795862.0,4.5 GB,openneuro,SRM Resting-state EEG,eeg,"[{""val"": 64, ""count"": 153}]","[{""val"": 1024.0, ""count"": 153}]",CC0,doi:10.18112/openneuro.ds003775.v1.2.1,8.0
ds003800,[],,Lahijanian2021_Auditory,13,24,2,Auditory,Clinical/Intervention,Dementia,1.4111111111111112,198443567.0,189.3 MB,openneuro,Auditory Gamma Entrainment,eeg,"[{""val"": 19, ""count"": 24}]","[{""val"": 250.0, ""count"": 24}]",CC0,10.18112/openneuro.ds003800.v1.0.0,4.0
ds003801,[],,Straetmans2021,20,20,1,,,,13.688888888888888,1233075132.0,1.1 GB,openneuro,Neural Tracking to go,eeg,"[{""val"": 24, ""count"": 20}]","[{""val"": 250.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds003801.v1.0.0,2.0
ds003805,[],,Lahijanian2021_Multisensory,1,1,1,,,,0.03333333333333333,9223975.0,8.8 MB,openneuro,Multisensory Gamma Entrainment,eeg,"[{""val"": 19, ""count"": 1}]","[{""val"": 500.0, ""count"": 1}]",CC0,10.18112/openneuro.ds003805.v1.0.0,3.0
ds003810,[],,Peterson2021_Motor_Imagery_vs,10,50,1,,,,5.188611111111111,72366316.0,69.0 MB,openneuro,Motor Imagery vs Rest - Low-Cost EEG System,eeg,"[{""val"": 15, ""count"": 50}]","[{""val"": 125.0, ""count"": 50}]",CC0,doi:10.18112/openneuro.ds003810.v2.0.2,2.0
ds003816,[],,Sun2024,48,1077,8,,,,161.7142777777778,57954833842.0,54.0 GB,openneuro,The Effect of Buddhism Derived Loving Kindness Meditation on Modulating EEG: Long-term and Short-term Effect,eeg,"[{""val"": 128, ""count"": 1077}]","[{""val"": 1000.0, ""count"": 1077}]",CC0,10.18112/openneuro.ds003816.v1.0.1,
ds003822,[],,Brown2021_Probabilistic_Learning,25,25,1,,,,12.877347222222221,6248471793.0,5.8 GB,openneuro,EEG: Probabilistic Learning with Affective Feedback: Exp #1,eeg,"[{""val"": 66, ""count"": 25}]","[{""val"": 500.0, ""count"": 25}]",CC0,10.18112/openneuro.ds003822.v1.1.0,0.0
ds003825,"[""THINGS"", ""THINGS_EEG""]",canonical,Grootswagers2021,50,50,1,,,,46.32270555555555,44258973478.0,41.2 GB,openneuro,"Human electroencephalography recordings from 50 subjects for 22,248 images from 1,854 object concepts",eeg,"[{""val"": 63, ""count"": 48}, {""val"": 128, ""count"": 2}]","[{""val"": 1000.0, ""count"": 50}]",CC0,10.18112/openneuro.ds003825.v1.1.0,2.0
ds003838,[],,Pavlov2021_pupillometry,65,130,2,,,,142.7015113888889,107600042705.0,100.2 GB,openneuro,"EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest",eeg,"[{""val"": 63, ""count"": 130}]","[{""val"": 1000.0, ""count"": 130}]",CC0,doi:10.18112/openneuro.ds003838.v1.0.6,7.0
ds003844,"[""RESPect_intraop""]",canonical,Zweiphenning2021,6,38,1,Resting State,Clinical/Intervention,Epilepsy,2.779091118706597,2795794610.0,2.6 GB,openneuro,Dataset Clinical Epilepsy iEEG to BIDS -RESPect_intraoperative_iEEG,ieeg,"[{""val"": 33, ""count"": 24}, {""val"": 64, ""count"": 9}, {""val"": 32, ""count"": 5}]","[{""val"": 2048.0, ""count"": 33}, {""val"": 256.0, ""count"": 5}]",CC0,10.18112/openneuro.ds003844.v1.0.1,0.0
ds003846,[],,Gehrke2021,19,50,1,,,,22.727667222222223,10480325277.0,9.8 GB,openneuro,Prediction Error,eeg,"[{""val"": 64, ""count"": 50}]","[{""val"": 500.0, ""count"": 50}]",CC0,doi:10.18112/openneuro.ds003846.v2.0.2,5.0
ds003848,"[""RESPect_longterm""]",canonical,Blooijs2021,6,22,6,Other,Clinical/Intervention,Epilepsy,20.275720486111112,69773848040.0,65.0 GB,openneuro,Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG,ieeg,"[{""val"": 133, ""count"": 18}, {""val"": 68, ""count"": 4}]","[{""val"": 2048.0, ""count"": 21}, {""val"": 512.0, ""count"": 1}]",CC0,10.18112/openneuro.ds003848.v1.0.3,1.0
ds003876,[],,Gunnarsdottir2021,39,54,3,Resting State,Clinical/Intervention,Epilepsy,5.757709854501917,5368598177.0,5.0 GB,openneuro,Epilepsy-iEEG-Interictal-Multicenter-Dataset,ieeg,"[{""val"": 128, ""count"": 10}, {""val"": 129, ""count"": 8}, {""val"": 86, ""count"": 4}, {""val"": 135, ""count"": 4}, {""val"": 98, ""count"": 4}, {""val"": 111, ""count"": 2}, {""val"": 101, ""count"": 2}, {""val"": 47, ""count"": 2}, {""val"": 110, ""count"": 2}, {""val"": 182, ""count"": 1}, {""val"": 118, ""count"": 1}, {""val"": 114, ""count"": 1}, {""val"": 170, ""count"": 1}, {""val"": 168, ""count"": 1}, {""val"": 95, ""count"": 1}, {""val"": 146, ""count"": 1}, {""val"": 121, ""count"": 1}, {""val"": 107, ""count"": 1}, {""val"": 46, ""count"": 1}, {""val"": 186, ""count"": 1}, {""val"": 125, ""count"": 1}, {""val"": 134, ""count"": 1}, {""val"": 193, ""count"": 1}, {""val"": 190, ""count"": 1}, {""val"": 147, ""count"": 1}]","[{""val"": 1000.0, ""count"": 25}, {""val"": 2000.0, ""count"": 7}, {""val"": 999.4121105232217, ""count"": 6}, {""val"": 1024.0, ""count"": 5}, {""val"": 999.9999999999999, ""count"": 4}, {""val"": 499.7071044492829, ""count"": 2}, {""val"": 1024.5997950800408, ""count"": 2}, {""val"": 500.0, ""count"": 2}, {""val"": 512.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds003876.v1.0.2,3.0
ds003885,[],,Shatek2021_E1,24,24,1,,,,27.061438888888887,49551741596.0,46.1 GB,openneuro,Capacity for movement is an organisational principle in object representations: EEG data from Experiment 1,eeg,"[{""val"": 128, ""count"": 24}]","[{""val"": 1000.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds003885.v1.0.7,2.0
ds003887,[],,Shatek2021_E2,24,24,1,,,,26.815155555555556,49100681711.0,45.7 GB,openneuro,Capacity for movement is an organisational principle in object representations: EEG data from Experiment 2,eeg,"[{""val"": 128, ""count"": 24}]","[{""val"": 1000.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds003887.v1.2.2,3.0
ds003922,[],,Lerousseau2021,14,164,3,Multisensory,Perception,Healthy,16.487458055555557,81285134094.0,75.7 GB,openneuro,Multisensory Correlation Detector,meg,"[{""val"": 342, ""count"": 128}, {""val"": 323, ""count"": 23}]","[{""val"": 1000.0, ""count"": 151}]",CC0,doi:10.18112/openneuro.ds003922.v1.0.1,1.0
ds003944,[],,Salisbury2021_First,82,82,1,,,,6.999305547125,6606397463.0,6.2 GB,openneuro,EEG: First Episode Psychosis vs. Control Resting Task 1,eeg,"[{""val"": 64, ""count"": 82}]","[{""val"": 1000.0, ""count"": 81}, {""val"": 3000.00030000003, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds003944.v1.0.1,7.0
ds003947,[],,Salisbury2021_First_Episode,61,61,1,,,,5.265971754930556,13466591790.0,12.5 GB,openneuro,EEG: First Episode Psychosis vs. Control Resting Task 2,eeg,"[{""val"": 64, ""count"": 61}]","[{""val"": 3000.00030000003, ""count"": 54}, {""val"": 1000.0, ""count"": 7}]",CC0,doi:10.18112/openneuro.ds003947.v1.0.1,8.0
ds003969,[],,Delorme2021,98,392,4,,,,66.53361111111111,58478045346.0,54.5 GB,openneuro,Meditation vs thinking task,eeg,"[{""val"": 79, ""count"": 294}, {""val"": 72, ""count"": 98}]","[{""val"": 1024.0, ""count"": 386}, {""val"": 2048.0, ""count"": 6}]",CC0,doi:10.18112/openneuro.ds003969.v1.0.0,7.0
ds003987,[],,Cavanagh2022_Amphetamine_trials_5,23,69,1,,,,52.076385814525466,27451677701.0,25.6 GB,openneuro,EEG: Amphetamine trials 5CCPT and Probabilistic Learning,eeg,"[{""val"": 71, ""count"": 69}]","[{""val"": 500.0930232558139, ""count"": 69}]",CC0,doi:10.18112/openneuro.ds003987.v1.0.0,1.0
ds004000,[],,Padee2022,43,86,2,,,,12.335,24161100490.0,22.5 GB,openneuro,Fribourg Ultimatum Game in Schizophrenia Study,eeg,"[{""val"": 132, ""count"": 86}]","[{""val"": 2048.0, ""count"": 86}]",CC0,doi:10.18112/openneuro.ds004000.v1.0.0,6.0
ds004010,"[""MAVIS""]",canonical,Waschke2022,24,24,1,,,,26.45728861111111,24844863656.0,23.1 GB,openneuro,MAVIS,eeg,"[{""val"": 64, ""count"": 24}]","[{""val"": 1000.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds004010.v1.0.0,0.0
ds004011,[],,Teichmann2022,22,132,1,Visual,Perception,Healthy,39.53718888888889,212729784057.0,198.1 GB,openneuro,The nature of neural object representations during dynamic occlusion,meg,"[{""val"": 309, ""count"": 132}]","[{""val"": 1200.0, ""count"": 132}]",CC0,doi:10.18112/openneuro.ds004011.v1.0.3,1.0
ds004012,"[""Rani2019""]",author_year,Rani2022,30,294,10,Unknown,Unknown,Healthy,15.016307222222222,84108428161.0,78.3 GB,openneuro,BRAR_NQ,meg,"[{""val"": 383, ""count"": 294}]","[{""val"": 1000.0, ""count"": 294}]",CC0,doi:10.18112/openneuro.ds004012.v1.0.0,1.0
ds004015,[],,Holtze2022_Attended,36,36,1,,,,47.28973222222222,6475869232.0,6.0 GB,openneuro,Attended speaker paradigm (cEEGrid data),eeg,"[{""val"": 18, ""count"": 36}]","[{""val"": 500.0, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds004015.v1.0.2,3.0
ds004017,[],,Damsgaard2022,21,63,0,Visual,Learning,Healthy,7.723333333333334,22471485461.0,20.9 GB,openneuro,Embodied Learning for Literacy EEG,eeg,"[{""val"": 65, ""count"": 63}]","[{""val"": 2048.0, ""count"": 63}]",CC0,doi:10.18112/openneuro.ds004017.v1.0.3,1.0
ds004018,[],,Grootswagers2022_RSVP,16,32,1,,,,0.7925055555555556,11334174445.0,10.6 GB,openneuro,EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz,eeg,"[{""val"": 63, ""count"": 32}]","[{""val"": 1000.0, ""count"": 32}]",CC0,doi:10.18112/openneuro.ds004018.v2.0.0,0.0
ds004019,[],,AlatorreCruz2022_Effect,62,62,1,Visual,Other,Obese,,18550696170.0,17.3 GB,openneuro,Effect of obesity on arithmetic processing in preteens with high and low math skills. An event-related potentials study,eeg,"[{""val"": 128, ""count"": 62}]","[{""val"": 500.0, ""count"": 62}]",CC0,doi:10.18112/openneuro.ds004019.v1.0.0,1.0
ds004022,[],,Lee2022,7,21,1,,,,,646522651.0,616.6 MB,openneuro,Multimodal EEG and fNIRS Biosignal Acquisition during Motor Imagery Tasks in Patients with Orthopedic Impairment,eeg,"[{""val"": 18, ""count"": 19}, {""val"": 16, ""count"": 2}]","[{""val"": 500.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds004022.v1.0.0,1.0
ds004024,[],,Pavon2022,13,497,3,,,,54.50040104166667,1096522005938.0,1021.2 GB,openneuro,"TMS-EEG-MRI-fMRI-DWI data on paired associative stimulation and connectivity (Shirley Ryan AbilityLab, Chicago, IL)",eeg,"[{""val"": 69, ""count"": 497}]","[{""val"": 20000.0, ""count"": 497}]",CC0,doi:10.18112/openneuro.ds004024.v1.0.1,1.0
ds004033,[],author_year,Scanlon2022,18,36,2,Auditory,Attention,Healthy,42.64460166666667,21270391132.0,19.8 GB,openneuro,Electrode walking study,eeg,"[{""val"": 67, ""count"": 36}]","[{""val"": 500.0, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds004033.v1.0.0,2.0
ds004040,[],,Cannard2022,13,26,1,,,,25.561944444444446,12440254661.0,11.6 GB,openneuro,Trance channeling EEG study,eeg,"[{""val"": 64, ""count"": 26}]","[{""val"": 512.0, ""count"": 26}]",CC0,doi:10.18112/openneuro.ds004040.v1.0.0,1.0
ds004043,[],,Moerel2022_time,20,20,1,,,,18.234355555555553,16582819425.0,15.4 GB,openneuro,The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes,eeg,"[{""val"": 63, ""count"": 20}]","[{""val"": 1000.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds004043.v1.1.0,1.0
ds004067,[],,Yoder2022,80,84,1,,,,59.642625,108218050324.0,100.8 GB,openneuro,Moral conviction and metacognitive ability shape multiple stages of information processing,eeg,"[{""val"": 63, ""count"": 84}]","[{""val"": 2000.0, ""count"": 84}]",CC0,doi:10.18112/openneuro.ds004067.v1.0.1,1.0
ds004075,[],author_year,Boncz2022,29,116,4,Unknown,Unknown,Unknown,12.554977777777777,7936059852.0,7.4 GB,openneuro,what_are_we_talking_about,eeg,"[{""val"": 64, ""count"": 115}]","[{""val"": 1000.0, ""count"": 116}]",CC0,doi:10.18112/openneuro.ds004075.v1.0.0,1.0
ds004078,[],,Wang2022_StudyBRAIN,12,720,1,Auditory,Other,Healthy,68.09214861111111,677637752173.0,631.1 GB,openneuro,A synchronized multimodal neuroimaging dataset to study brain language processing,meg,"[{""val"": 328, ""count"": 720}]","[{""val"": 1000.0, ""count"": 720}]",CC0,doi:10.18112/openneuro.ds004078.v1.0.4,4.0
ds004080,"[""RESPect_CCEP""]",canonical,Blooijs2023_CCEP_ECoG,74,117,1,Other,Clinical/Intervention,Epilepsy,89.39102945963542,288953106451.0,269.1 GB,openneuro,CCEP ECoG dataset across age 4-51,ieeg,"[{""val"": 133, ""count"": 70}, {""val"": 68, ""count"": 18}, {""val"": 130, ""count"": 13}, {""val"": 98, ""count"": 4}, {""val"": 131, ""count"": 4}, {""val"": 96, ""count"": 4}, {""val"": 64, ""count"": 2}, {""val"": 94, ""count"": 1}, {""val"": 93, ""count"": 1}]","[{""val"": 2048.0, ""count"": 112}, {""val"": 512.0, ""count"": 5}]",CC0,doi:10.18112/openneuro.ds004080.v1.2.4,2.0
ds004100,"[""HUPiEEG""]",canonical,Bernabei2022,57,319,2,Other,Clinical/Intervention,Epilepsy,25.717898949652778,14196260504.0,13.2 GB,openneuro,HUP iEEG Epilepsy Dataset,ieeg,"[{""val"": 122, ""count"": 21}, {""val"": 128, ""count"": 18}, {""val"": 118, ""count"": 17}, {""val"": 172, ""count"": 15}, {""val"": 126, ""count"": 14}, {""val"": 104, ""count"": 13}, {""val"": 82, ""count"": 12}, {""val"": 127, ""count"": 12}, {""val"": 180, ""count"": 12}, {""val"": 96, ""count"": 12}, {""val"": 92, ""count"": 7}, {""val"": 80, ""count"": 7}, {""val"": 190, ""count"": 7}, {""val"": 108, ""count"": 7}, {""val"": 74, ""count"": 7}, {""val"": 121, ""count"": 7}, {""val"": 136, ""count"": 7}, {""val"": 109, ""count"": 7}, {""val"": 117, ""count"": 7}, {""val"": 102, ""count"": 7}, {""val"": 174, ""count"": 7}, {""val"": 149, ""count"": 7}, {""val"": 120, ""count"": 7}, {""val"": 163, ""count"": 6}, {""val"": 98, ""count"": 6}, {""val"": 63, ""count"": 5}, {""val"": 186, ""count"": 5}, {""val"": 162, ""count"": 5}, {""val"": 100, ""count"": 5}, {""val"": 164, ""count"": 5}, {""val"": 88, ""count"": 5}, {""val"": 59, ""count"": 5}, {""val"": 116, ""count"": 5}, {""val"": 52, ""count"": 5}, {""val"": 71, ""count"": 5}, {""val"": 105, ""count"": 4}, {""val"": 90, ""count"": 4}, {""val"": 61, ""count"": 4}, {""val"": 85, ""count"": 3}, {""val"": 94, ""count"": 2}, {""val"": 192, ""count"": 2}, {""val"": 232, ""count"": 1}]","[{""val"": 512.0, ""count"": 165}, {""val"": 1024.0, ""count"": 78}, {""val"": 500.0, ""count"": 69}, {""val"": 256.0, ""count"": 7}]",CC0,doi:10.18112/openneuro.ds004100.v1.1.3,21.0
ds004105,"[""BCIT_Auditory_Cueing""]",canonical,Garcia2022,17,34,1,Multisensory,Attention,Healthy,,21876513335.0,20.4 GB,openneuro,BCIT Auditory Cueing,eeg,"[{""val"": 74, ""count"": 34}]","[{""val"": 1024.0, ""count"": 34}]",CC0,doi:10.18112/openneuro.ds004105.v1.0.0,0.0
ds004106,"[""BCITAdvancedGuardDuty""]",canonical,Touryan2022,27,29,1,Visual,Attention,Healthy,,72590770632.0,67.6 GB,openneuro,BCIT Advanced Guard Duty,eeg,"[{""val"": 262, ""count"": 29}]","[{""val"": 1024.0, ""count"": 29}]",CC0,doi:10.18112/openneuro.ds004106.v1.0.0,0.0
ds004107,"[""Weisend2007""]",author_year,Weisend2022,9,89,6,Multisensory,Other,Healthy,23.117616255933115,82901417999.0,77.2 GB,openneuro,MIND DATA,meg,"[{""val"": 318, ""count"": 84}, {""val"": 317, ""count"": 5}]","[{""val"": 1792.8858642578125, ""count"": 57}, {""val"": 1250.0, ""count"": 32}]",CC0,doi:10.18112/openneuro.ds004107.v1.0.0,1.0
ds004117,[],,Onton2022,23,85,1,,,,15.464153618985737,6229874415.0,5.8 GB,openneuro,Sternberg Working Memory,eeg,"[{""val"": 71, ""count"": 85}]","[{""val"": 250.0, ""count"": 47}, {""val"": 500.0, ""count"": 24}, {""val"": 500.059, ""count"": 11}, {""val"": 1000.0, ""count"": 3}]",CC0,doi:10.18112/openneuro.ds004117.v1.0.1,2.0
ds004118,"[""Touryan1999""]",author_year,Touryan2022_BCIT_Calibration,156,247,1,Visual,Attention,Healthy,,133504200631.0,124.3 GB,openneuro,BCIT Calibration Driving,eeg,"[{""val"": 266, ""count"": 128}, {""val"": 74, ""count"": 119}]","[{""val"": 1024.0, ""count"": 226}, {""val"": 2048.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds004118.v1.0.1,0.0
ds004119,"[""BCIT""]",canonical,Touryan2022_BCIT_Basic,21,22,1,Visual,Attention,Healthy,,59203206625.0,55.1 GB,openneuro,BCIT Basic Guard Duty,eeg,"[{""val"": 262, ""count"": 22}]","[{""val"": 1024.0, ""count"": 22}]",CC0,doi:10.18112/openneuro.ds004119.v1.0.0,0.0
ds004120,"[""BCITBaselineDriving""]",canonical,Touryan2022_BCIT_Baseline,109,131,1,Visual,Attention,Healthy,,324791400217.0,302.5 GB,openneuro,BCIT Baseline Driving,eeg,"[{""val"": 266, ""count"": 81}, {""val"": 74, ""count"": 50}]","[{""val"": 1024.0, ""count"": 109}, {""val"": 2048.0, ""count"": 22}]",CC0,doi:10.18112/openneuro.ds004120.v1.0.0,0.0
ds004121,"[""BCITMindWandering""]",canonical,Touryan2022_BCIT_Mind,21,60,1,Multisensory,Attention,Healthy,,25671365743.0,23.9 GB,openneuro,BCIT Mind Wandering,eeg,"[{""val"": 74, ""count"": 60}]","[{""val"": 1024.0, ""count"": 60}]",CC0,doi:10.18112/openneuro.ds004121.v1.0.0,0.0
ds004122,[],,Touryan2022_BCIT_Speed,32,63,1,Visual,Attention,Healthy,,38826707124.0,36.2 GB,openneuro,BCIT Speed Control,eeg,"[{""val"": 74, ""count"": 63}]","[{""val"": 1024.0, ""count"": 63}]",CC0,doi:10.18112/openneuro.ds004122.v1.0.0,0.0
ds004123,"[""BCIT_Traffic_Complexity""]",canonical,Touryan2022_BCIT_Traffic,29,30,1,Visual,Attention,Healthy,,18814102906.0,17.5 GB,openneuro,BCIT Traffic Complexity,eeg,"[{""val"": 74, ""count"": 30}]","[{""val"": 1024.0, ""count"": 30}]",CC0,doi:10.18112/openneuro.ds004123.v1.0.0,0.0
ds004127,[],,Abrego2022,8,73,11,Tactile,Other,Other,6.083819444444444,201353276068.0,187.5 GB,openneuro,Somatosensory Cortex Rat DISC Data,ieeg,"[{""val"": 128, ""count"": 28}, {""val"": 110, ""count"": 9}, {""val"": 102, ""count"": 9}, {""val"": 104, ""count"": 9}, {""val"": 112, ""count"": 9}, {""val"": 105, ""count"": 6}, {""val"": 106, ""count"": 2}, {""val"": 111, ""count"": 1}]","[{""val"": 20000.0, ""count"": 73}]",CC0,doi:10.18112/openneuro.ds004127.v3.0.0,1.0
ds004147,[],,Hassall2022_Average,12,12,1,Visual,Learning,Healthy,9.60806111111111,4291179548.0,4.0 GB,openneuro,Average Task Value,eeg,"[{""val"": 31, ""count"": 12}]","[{""val"": 1000.0, ""count"": 12}]",CC0,doi:10.18112/openneuro.ds004147.v1.0.2,2.0
ds004148,[],,Wang2022_test_retest_resting,60,900,5,Other,Other,Healthy,75.0,32936811897.0,30.7 GB,openneuro,A test-retest resting and cognitive state EEG dataset,eeg,"[{""val"": 61, ""count"": 900}]","[{""val"": 500.0, ""count"": 900}]",CC0,doi:10.18112/openneuro.ds004148.v1.0.0,12.0
ds004151,[],,AlatorreCruz2022_Effect_obesity,57,57,1,Visual,Attention,Obese,,24845636935.0,23.1 GB,openneuro,"Effect of obesity on inhibitory control in preadolescents during stop-signal 
task. An event-related potentials study",eeg,"[{""val"": 128, ""count"": 57}]","[{""val"": 500.0, ""count"": 57}]",CC0,doi:10.18112/openneuro.ds004151.v1.0.0,1.0
ds004152,[],,Hassall2022_Drum,21,21,1,,,,11.451405555555555,5118532675.0,4.8 GB,openneuro,Drum Trainer,eeg,"[{""val"": 31, ""count"": 21}]","[{""val"": 1000.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds004152.v1.1.2,1.0
ds004166,[],,Li2022,71,213,1,Visual,Learning,Healthy,,83074443348.0,77.4 GB,openneuro,"Effects of Forward and Backward Span Trainings on Working Memory: Evidence from a Randomized, Controlled Trial",eeg,[],[],CC0,doi:10.18112/openneuro.ds004166.v1.0.0,1.0
ds004194,[],,Groen2022,14,209,7,Visual,Perception,Epilepsy,6.3436859809027775,8405758704.0,7.8 GB,openneuro,Visual ECoG dataset,ieeg,"[{""val"": 265, ""count"": 66}, {""val"": 133, ""count"": 20}, {""val"": 173, ""count"": 20}, {""val"": 124, ""count"": 14}, {""val"": 140, ""count"": 12}, {""val"": 74, ""count"": 12}, {""val"": 206, ""count"": 12}, {""val"": 160, ""count"": 10}, {""val"": 129, ""count"": 10}, {""val"": 111, ""count"": 10}, {""val"": 91, ""count"": 10}, {""val"": 106, ""count"": 7}, {""val"": 61, ""count"": 6}]","[{""val"": 512.0, ""count"": 197}, {""val"": 2048.0, ""count"": 12}]",CC0,doi:10.18112/openneuro.ds004194.v3.0.0,4.0
ds004196,[],,Liwicki2022,4,4,1,,,,1.511111111111111,10022897658.0,9.3 GB,openneuro,Bimodal dataset on Inner speech,eeg,"[{""val"": 64, ""count"": 4}]","[{""val"": 512.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds004196.v2.0.2,1.0
ds004200,[],,Hassall2022_Temporal,20,20,1,,,,14.122722222222222,7740008000.0,7.2 GB,openneuro,Temporal Scaling,eeg,"[{""val"": 37, ""count"": 20}]","[{""val"": 1000.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds004200.v1.0.1,1.0
ds004212,"[""THINGS_MEG"", ""THINGSMEG""]",canonical,Hebart2022,5,500,1,Visual,Perception,Healthy,45.239891666666665,255218599475.0,237.7 GB,openneuro,THINGS-MEG,meg,"[{""val"": 310, ""count"": 470}]","[{""val"": 1200.0, ""count"": 470}]",CC0,doi:10.18112/openneuro.ds004212.v3.0.0,3.0
ds004229,[],,Mittag2022,2,3,2,Auditory,Perception,Dyslexia,0.33138842592592593,1908935731.0,1.8 GB,openneuro,amnoise,meg,"[{""val"": 332, ""count"": 2}]","[{""val"": 1200.0, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds004229.v1.0.3,0.0
ds004252,[],,Moerel2022_Rotation,1,1,1,,,,0.7596333333333333,1392179512.0,1.3 GB,openneuro,Rotation-tolerant representations elucidate the time course of high-level object processing,eeg,"[{""val"": 127, ""count"": 1}]","[{""val"": 1000.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004252.v1.0.2,1.0
ds004256,[],,Bialas2022,53,53,2,Auditory,Perception,Healthy,42.33703611111111,19516271222.0,18.2 GB,openneuro,Encoding of Sound Source Elevation in Human Cortex,eeg,"[{""val"": 64, ""count"": 53}]","[{""val"": 500.0, ""count"": 53}]",CC0,doi:10.18112/openneuro.ds004256.v1.0.5,0.0
ds004262,[],,Hassall2022_Continuous,21,21,1,,,,8.347866666666667,3728371669.0,3.5 GB,openneuro,Continuous Feedback Processing,eeg,"[{""val"": 31, ""count"": 21}]","[{""val"": 1000.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds004262.v1.0.0,1.0
ds004264,[],,Hassall2022_Steer,21,21,1,,,,7.460555555555556,3546002190.0,3.3 GB,openneuro,Steer the Ship,eeg,"[{""val"": 31, ""count"": 21}]","[{""val"": 1000.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds004264.v1.1.0,0.0
ds004276,[],author_year,Gaston2022,19,19,2,Auditory,Perception,Healthy,5.2202725,12467037085.0,11.6 GB,openneuro,Auditory single word recognition in MEG,meg,"[{""val"": 193, ""count"": 19}]","[{""val"": 1000.0, ""count"": 19}]",CC0,doi:10.18112/openneuro.ds004276.v1.0.0,2.0
ds004278,"[""Kidder2024""]",author_year,Kidder2022,30,30,1,Unknown,Memory,Healthy,15.533326388888888,82360041747.0,76.7 GB,openneuro,Sustained Neural Representations of Personally Familiar People and Places During Cued Recall,meg,"[{""val"": 306, ""count"": 30}]","[{""val"": 1200.0, ""count"": 30}]",CC0,doi:10.18112/openneuro.ds004278.v1.0.1,0.0
ds004279,[],,Araya2022,56,60,1,,,,53.728649999999995,27082275332.0,25.2 GB,openneuro,Large Spanish EEG,eeg,"[{""val"": 69, ""count"": 60}]","[{""val"": 1000.0, ""count"": 60}]",CC0,doi:10.18112/openneuro.ds004279.v1.1.2,1.0
ds004284,[],,Veillette2022,18,18,1,,,,9.454245,17562665263.0,16.4 GB,openneuro,eeg-neuroforecasting,eeg,"[{""val"": 129, ""count"": 18}]","[{""val"": 1000.0, ""count"": 18}]",CC0,doi:10.18112/openneuro.ds004284.v1.0.0,1.0
ds004295,[],,Stolz2022,26,26,1,,,,34.312777777777775,33829431825.0,31.5 GB,openneuro,Reward gain and punishment avoidance reversal learning,eeg,"[{""val"": 66, ""count"": 26}]","[{""val"": 1024.0, ""count"": 25}, {""val"": 512.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004295.v1.0.0,1.0
ds004306,[],,Wilson2022,12,15,1,,,,18.18320014105903,686761341.0,654.9 MB,openneuro,EEG Semantic Imagination and Perception Dataset,eeg,"[{""val"": 128, ""count"": 15}]","[{""val"": 1024.0, ""count"": 15}]",CC0,doi:10.18112/openneuro.ds004306.v1.0.2,1.0
ds004315,[],,Cavanagh2022_E1,50,50,1,,,,21.10388888888889,10532286604.0,9.8 GB,openneuro,"Mood Manipulation and PST, Experiment 1",eeg,"[{""val"": 66, ""count"": 50}]","[{""val"": 500.0, ""count"": 50}]",CC0,doi:10.18112/openneuro.ds004315.v1.0.0,1.0
ds004317,[],,Cavanagh2022_E2,50,50,1,,,,37.76679166666667,19639199295.0,18.3 GB,openneuro,"Mood Manipulation and PST, Experiment 2",eeg,"[{""val"": 66, ""count"": 50}]","[{""val"": 500.0, ""count"": 50}]",CC0,doi:10.18112/openneuro.ds004317.v1.0.3,1.0
ds004324,"[""ToonFaces""]",canonical,Chacon2022,26,26,1,,,,19.21581888888889,2637688659.0,2.5 GB,openneuro,ToonFaces,eeg,"[{""val"": 38, ""count"": 26}]","[{""val"": 500.0, ""count"": 26}]",CC0,doi:10.18112/openneuro.ds004324.v1.0.0,0.0
ds004330,[],,Singer2022,30,270,1,Visual,Perception,Healthy,36.68270277777778,165027293648.0,153.7 GB,openneuro,The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG),meg,"[{""val"": 310, ""count"": 270}]","[{""val"": 1000.0, ""count"": 270}]",CC0,doi:10.18112/openneuro.ds004330.v1.0.0,1.0
ds004346,"[""FLUX""]",canonical,Ferrante2022,1,3,1,Unknown,Attention,Healthy,0.803055,3857854581.0,3.6 GB,openneuro,FLUX: A pipeline for MEG analysis,meg,"[{""val"": 343, ""count"": 2}]","[{""val"": 1000.0, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds004346.v1.0.8,0.0
ds004347,[],,Makin2022,24,24,1,,,,6.375555555555556,2574266001.0,2.4 GB,openneuro,Symmetry perception and affective responses: a combined EEG/EMG study,eeg,"[{""val"": 72, ""count"": 24}]","[{""val"": 512.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds004347.v1.0.0,0.0
ds004348,"[""EESM17""]",canonical,Mikkelsen2022,9,18,2,,,,17.527765277777778,8793613791.0,8.2 GB,openneuro,Ear-EEG Sleep Monitoring 2017 (EESM17),eeg,"[{""val"": 34, ""count"": 9}]","[{""val"": 200.0, ""count"": 9}]",CC0,doi:10.18112/openneuro.ds004348.v1.0.5,0.0
ds004350,[],,Delorme2022,24,240,5,,,,41.21611111111111,10139679269.0,9.4 GB,openneuro,Executive Functionning Study for Assessing the Effect of Neurofeedback,eeg,"[{""val"": 64, ""count"": 240}]","[{""val"": 256.0, ""count"": 240}]",CC0,doi:10.18112/openneuro.ds004350.v2.0.0,1.0
ds004356,[],,Shan2022,22,24,1,,,,46.266555555555556,228796285688.0,213.1 GB,openneuro,Subcortical responses to music and speech are alike while cortical responses diverge,eeg,"[{""val"": 34, ""count"": 24}]","[{""val"": 10000.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds004356.v2.2.1,2.0
ds004357,[],,Grootswagers2022_EEG,16,16,1,,,,11.307033333333333,20769757800.0,19.3 GB,openneuro,Features-EEG,eeg,"[{""val"": 63, ""count"": 16}]","[{""val"": 1000.0, ""count"": 16}]",CC0,doi:10.18112/openneuro.ds004357.v1.0.1,2.0
ds004362,"[""PhysionetMI"", ""EEGMotorMovementImagery""]",canonical,Schalk2022,109,1526,1,,,,48.534444444444446,8347280653.0,7.8 GB,openneuro,EEG Motor Movement/Imagery Dataset,eeg,"[{""val"": 64, ""count"": 1526}]","[{""val"": 160.0, ""count"": 1490}, {""val"": 128.0, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds004362.v1.0.0,2.0
ds004367,[],,Rouy2022_Meta,40,40,1,,,,24.809597685185185,30039343360.0,28.0 GB,openneuro,Meta-rdk: Raw EEG data,eeg,"[{""val"": 68, ""count"": 40}]","[{""val"": 1200.0, ""count"": 40}]",CC0,doi:10.18112/openneuro.ds004367.v1.0.2,1.0
ds004368,[],,Rouy2022_Meta_rdk,39,40,1,,,,0.03333333333333333,1045574363.0,997.1 MB,openneuro,Meta-rdk: Preprocessed EEG data,eeg,"[{""val"": 63, ""count"": 40}]","[{""val"": 128.0, ""count"": 40}]",CC0,doi:10.18112/openneuro.ds004368.v1.0.2,0.0
ds004369,[],,Holtze2022_Blink,41,41,1,,,,37.333333333333336,2182344547.0,2.0 GB,openneuro,Blink-Pause-Relation (Competing Speaker Paradigm),eeg,"[{""val"": 7, ""count"": 41}]","[{""val"": 500.0, ""count"": 41}]",CC0,doi:10.18112/openneuro.ds004369.v1.0.1,1.0
ds004370,"[""PRIOS""]",canonical,Blooijs2022_PRIOS,7,15,2,Anesthesia,Clinical/Intervention,Surgery,10.201727430555556,29584367694.0,27.6 GB,openneuro,PRIOS,ieeg,"[{""val"": 133, ""count"": 7}, {""val"": 68, ""count"": 6}, {""val"": 64, ""count"": 2}]","[{""val"": 2048.0, ""count"": 15}]",CC0,doi:10.18112/openneuro.ds004370.v1.0.2,1.0
ds004381,[],,Selmin2022,18,437,1,,,,11.815105777777777,8296777421.0,7.7 GB,openneuro,Intraoperative EEG dataset during medianus-tibialis stimulation with 8 different rates,eeg,"[{""val"": 4, ""count"": 333}, {""val"": 8, ""count"": 47}, {""val"": 5, ""count"": 26}, {""val"": 7, ""count"": 26}, {""val"": 10, ""count"": 5}]","[{""val"": 20000.0, ""count"": 437}]",CC0,doi:10.18112/openneuro.ds004381.v1.0.2,2.0
ds004388,[],,Nierula2023_Somatosensory,40,399,3,Tactile,Perception,Healthy,43.48990325,732876216370.0,682.5 GB,openneuro,Somatosensory evoked potentials in the human spinal cord to mixed nerve stimulation,eeg,"[{""val"": 115, ""count"": 319}, {""val"": 114, ""count"": 80}]","[{""val"": 10000.0, ""count"": 399}]",CC0,doi:10.18112/openneuro.ds004388.v1.0.0,3.0
ds004389,[],,Nierula2023_Somatosensory_evoked,26,260,4,Tactile,Perception,Healthy,30.655941527777777,404264478546.0,376.5 GB,openneuro,Somatosensory evoked potentials in the human spinal cord to mixed and sensory nerve stimulation,eeg,"[{""val"": 90, ""count"": 260}]","[{""val"": 10000.0, ""count"": 260}]",CC0,doi:10.18112/openneuro.ds004389.v1.0.0,2.0
ds004395,"[""PEERS""]",canonical,Kahana2023,364,6483,3,Visual,Memory,Healthy,9115.806919930556,9583322399138.0,8.7 TB,openneuro,Penn Electrophysiology of Encoding and Retrieval Study (PEERS),eeg,"[{""val"": 129, ""count"": 4980}, {""val"": 137, ""count"": 1490}, {""val"": 144, ""count"": 11}, {""val"": 272, ""count"": 2}]","[{""val"": 500.0, ""count"": 4946}, {""val"": 2048.0, ""count"": 1466}, {""val"": 512.0, ""count"": 28}, {""val"": 250.0, ""count"": 17}, {""val"": 1000.0, ""count"": 15}, {""val"": 1024.0, ""count"": 11}]",CC0,doi:10.18112/openneuro.ds004395.v2.0.0,6.0
ds004398,"[""Wimmer2024""]",author_year,Wimmer2023,1,1,1,Visual,Unknown,Unknown,,1373056065.0,1.3 GB,openneuro,planmemreplay,meg,"[{""val"": 305, ""count"": 1}]","[{""val"": 600.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004398.v1.0.0,1.0
ds004408,[],,Liberto2023,19,380,1,,,,20.59452419704861,20083249467.0,18.7 GB,openneuro,EEG responses to continuous naturalistic speech,eeg,"[{""val"": 128, ""count"": 380}]","[{""val"": 512.0, ""count"": 380}]",CC0,doi:10.18112/openneuro.ds004408.v1.0.8,1.0
ds004444,"[""BMI_HDEEG_D1""]",canonical,Iwama2023_D1,30,465,1,,,,55.68745555555555,52204973510.0,48.6 GB,openneuro,The BMI-HDEEG dataset 1,eeg,"[{""val"": 129, ""count"": 465}]","[{""val"": 1000.0, ""count"": 465}]",CC0,doi:10.18112/openneuro.ds004444.v1.0.1,1.0
ds004446,"[""BMI_HDEEG_D2""]",canonical,Iwama2023_D2,30,237,1,,,,33.486105555555554,31382983993.0,29.2 GB,openneuro,The BMI-HDEEG dataset 2,eeg,"[{""val"": 129, ""count"": 237}]","[{""val"": 1000.0, ""count"": 237}]",CC0,doi:10.18112/openneuro.ds004446.v1.0.1,1.0
ds004447,"[""BMI_HDEEG_D3""]",canonical,Iwama2023_D3,22,418,1,,,,23.554360555555558,22253513860.0,20.7 GB,openneuro,The BMI-HDEEG dataset 3,eeg,"[{""val"": 129, ""count"": 418}]","[{""val"": 1000.0, ""count"": 418}]",CC0,doi:10.18112/openneuro.ds004447.v1.0.1,1.0
ds004448,"[""BMI_HDEEG_D4""]",canonical,Iwama2023_D4,56,280,1,,,,43.732013888888886,40980947792.0,38.2 GB,openneuro,The BMI-HDEEG dataset 4,eeg,"[{""val"": 129, ""count"": 280}]","[{""val"": 1000.0, ""count"": 280}]",CC0,doi:10.18112/openneuro.ds004448.v1.0.2,1.0
ds004457,"[""Huang2022""]",author_year,Huang2023,5,5,1,Other,Clinical/Intervention,Surgery,5.6259163411458335,11705318065.0,10.9 GB,openneuro,Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex,ieeg,"[{""val"": 206, ""count"": 1}, {""val"": 178, ""count"": 1}, {""val"": 194, ""count"": 1}, {""val"": 135, ""count"": 1}, {""val"": 192, ""count"": 1}]","[{""val"": 2048.0, ""count"": 5}]",CC0,doi:10.18112/openneuro.ds004457.v1.0.1,3.0
ds004460,[],,Gramann2023,20,40,1,,,,27.49372888888889,63428693430.0,59.1 GB,openneuro,EEG and motion capture data set for a full-body/joystick rotation task,eeg,"[{""val"": 160, ""count"": 40}]","[{""val"": 1000.0, ""count"": 40}]",CC0,doi:10.18112/openneuro.ds004460.v1.1.0,1.0
ds004473,"[""Rockhill2022""]",author_year,Rockhill2023,8,8,1,Visual,Motor,Epilepsy,6.9854377764705875,6767566336.0,6.3 GB,openneuro,sEEG Forced Two-Choice Task,ieeg,"[{""val"": 129, ""count"": 8}]","[{""val"": 999.4121105232217, ""count"": 8}]",CC0,doi:10.18112/openneuro.ds004473.v1.0.1,1.0
ds004475,[],,Jacobsen2023,30,30,1,,,,26.898611111111112,52115324993.0,48.5 GB,openneuro,Mobile EEG split-belt walking study,eeg,"[{""val"": 260, ""count"": 5}, {""val"": 250, ""count"": 3}, {""val"": 263, ""count"": 3}, {""val"": 255, ""count"": 3}, {""val"": 257, ""count"": 3}, {""val"": 258, ""count"": 3}, {""val"": 259, ""count"": 2}, {""val"": 256, ""count"": 1}, {""val"": 249, ""count"": 1}, {""val"": 252, ""count"": 1}, {""val"": 265, ""count"": 1}, {""val"": 254, ""count"": 1}, {""val"": 253, ""count"": 1}, {""val"": 261, ""count"": 1}, {""val"": 262, ""count"": 1}]","[{""val"": 512.0, ""count"": 30}]",CC0,doi:10.18112/openneuro.ds004475.v1.0.3,2.0
ds004477,[],,Papastylianou2023,9,9,1,,,,13.557221001519098,23989925051.0,22.3 GB,openneuro,PES - Pandemic Emergency Scenario,eeg,"[{""val"": 80, ""count"": 9}]","[{""val"": 2048.0, ""count"": 9}]",CC0,doi:10.18112/openneuro.ds004477.v1.0.2,0.0
ds004483,"[""ABSeqMEG""]",canonical,Planton2023,19,282,1,Auditory,Memory,Healthy,16.683036666666666,25146610011.0,23.4 GB,openneuro,ABSeqMEG,meg,"[{""val"": 396, ""count"": 263}]","[{""val"": 250.0, ""count"": 263}]",CC0,doi:10.18112/openneuro.ds004483.v1.0.0,2.0
ds004502,"[""Penalver2024""]",author_year,Penalver2023,48,48,1,Unknown,Attention,Healthy,92.62319444444445,63828222669.0,59.4 GB,openneuro,Anticipatory differences between Attention and Expectation,eeg,"[{""val"": 63, ""count"": 44}, {""val"": 65, ""count"": 4}]","[{""val"": 1000.0, ""count"": 44}, {""val"": 500.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds004502.v1.0.1,3.0
ds004504,[],,Miltiadous2023,88,88,1,,,,19.608416666666667,2829897024.0,2.6 GB,openneuro,"A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects",eeg,"[{""val"": 19, ""count"": 88}]","[{""val"": 500.0, ""count"": 88}]",CC0,doi:10.18112/openneuro.ds004504.v1.0.8,55.0
ds004505,[],,Studnicki2023,25,25,1,,,,30.398184444444443,37123164373.0,34.6 GB,openneuro,Real World Table Tennis,eeg,"[{""val"": 313, ""count"": 13}, {""val"": 270, ""count"": 4}, {""val"": 299, ""count"": 2}, {""val"": 312, ""count"": 2}, {""val"": 303, ""count"": 1}, {""val"": 327, ""count"": 1}, {""val"": 326, ""count"": 1}, {""val"": 340, ""count"": 1}]","[{""val"": 250.0, ""count"": 25}]",CC0,doi:10.18112/openneuro.ds004505.v1.0.4,5.0
ds004511,[],,Makowski2023_Deception,45,134,3,Visual,Decision-making,Healthy,64.14129787037037,217194708760.0,202.3 GB,openneuro,Deception_data,eeg,"[{""val"": 139, ""count"": 134}]","[{""val"": 3000.0, ""count"": 134}]",CC0,doi:10.18112/openneuro.ds004511.v1.0.2,2.0
ds004514,[],,Rybar2023_Simultaneous,12,24,2,Multisensory,Other,Healthy,29.3083050390625,25930603322.0,24.1 GB,openneuro,Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools,"eeg, fnirs","[{""val"": 80, ""count"": 12}, {""val"": 22, ""count"": 6}, {""val"": 28, ""count"": 6}]","[{""val"": 2048.0, ""count"": 12}, {""val"": 7.8125, ""count"": 6}, {""val"": 8.928571428571429, ""count"": 6}]",CC0,doi:10.18112/openneuro.ds004514.v1.1.2,
ds004515,[],,Singh2023,54,54,1,,,,20.609680555555556,10177195129.0,9.5 GB,openneuro,EEG: Alcohol imagery reinforcement learning task with light and heavy drinker participants,eeg,"[{""val"": 66, ""count"": 54}]","[{""val"": 500.0, ""count"": 54}]",CC0,doi:10.18112/openneuro.ds004515.v1.0.0,4.0
ds004517,[],,Rybar2023_semantic,7,7,1,Visual,Other,Healthy,7.691110161675347,13610005494.0,12.7 GB,openneuro,EEG recordings for semantic decoding of imagined animals and tools during auditory imagery task,eeg,"[{""val"": 80, ""count"": 7}]","[{""val"": 2048.0, ""count"": 7}]",CC0,doi:10.18112/openneuro.ds004517.v1.0.2,
ds004519,"[""Ester2022""]",author_year,Ester2023_Internal,40,40,1,Visual,Attention,Healthy,0.06666666666666667,13486847571.0,12.6 GB,openneuro,Internal selective attention is delayed by competition between endogenous and exogenous factors,eeg,"[{""val"": 62, ""count"": 40}]","[{""val"": 250.0, ""count"": 40}]",CC0,doi:10.18112/openneuro.ds004519.v1.0.1,3.0
ds004520,"[""Ester2024_E2""]",author_year,Ester2023_Changes,33,33,1,Visual,Memory,Healthy,0.055,11175907697.0,10.4 GB,openneuro,Changes in behavioral priority influence the accessibility of working memory content - Experiment 2,eeg,"[{""val"": 62, ""count"": 33}]","[{""val"": 250.0, ""count"": 33}]",CC0,doi:10.18112/openneuro.ds004520.v1.0.1,3.0
ds004521,"[""Ester2024_E1""]",author_year,Ester2023_Changes_behavioral,34,34,1,Unknown,Memory,Healthy,0.056666666666666664,11470005753.0,10.7 GB,openneuro,Changes in behavioral priority influence the accessibility of working memory content - Experiment 1,eeg,"[{""val"": 62, ""count"": 34}]","[{""val"": 250.0, ""count"": 34}]",CC0,doi:10.18112/openneuro.ds004521.v1.0.1,3.0
ds004532,[],,Cavanagh2023,110,137,1,,,,49.65127888888889,23367840317.0,21.8 GB,openneuro,EEG: Probabilistic Selection Task (PST) + PST with Cabergoline Challenge,eeg,"[{""val"": 64, ""count"": 137}]","[{""val"": 500.0, ""count"": 137}]",CC0,doi:10.18112/openneuro.ds004532.v1.2.0,0.0
ds004541,"[""Ferron2019""]",author_year,Ferron2023,8,18,1,Anesthesia,Clinical/Intervention,Surgery,12.130006388888889,3112226120.0,2.9 GB,openneuro,Multimodal EEG-fNIRS data from patients undergoing general anesthesia,"eeg, fnirs","[{""val"": 59, ""count"": 9}, {""val"": 40, ""count"": 5}, {""val"": 30, ""count"": 3}, {""val"": 38, ""count"": 1}]","[{""val"": 1000.0, ""count"": 9}, {""val"": 7.8125, ""count"": 9}]",CC0,doi:10.18112/openneuro.ds004541.v1.0.0,
ds004551,"[""Sakakura2025""]",author_year,Sakakura2023_children_slow_wave,114,125,1,Sleep,Sleep,Epilepsy,1.0367611111111112,74017379206.0,68.9 GB,openneuro,iEEG on children during slow wave sleep,ieeg,"[{""val"": 128, ""count"": 82}, {""val"": 112, ""count"": 5}, {""val"": 118, ""count"": 3}, {""val"": 138, ""count"": 3}, {""val"": 104, ""count"": 2}, {""val"": 134, ""count"": 2}, {""val"": 110, ""count"": 2}, {""val"": 124, ""count"": 2}, {""val"": 108, ""count"": 2}, {""val"": 142, ""count"": 2}, {""val"": 148, ""count"": 2}, {""val"": 122, ""count"": 2}, {""val"": 102, ""count"": 2}, {""val"": 130, ""count"": 2}, {""val"": 144, ""count"": 2}, {""val"": 120, ""count"": 1}, {""val"": 96, ""count"": 1}, {""val"": 106, ""count"": 1}, {""val"": 146, ""count"": 1}, {""val"": 84, ""count"": 1}, {""val"": 116, ""count"": 1}, {""val"": 136, ""count"": 1}, {""val"": 126, ""count"": 1}, {""val"": 132, ""count"": 1}, {""val"": 58, ""count"": 1}]","[{""val"": 1000.0, ""count"": 125}]",CC0,doi:10.18112/openneuro.ds004551.v1.0.6,3.0
ds004554,[],,Volpert2023,16,16,1,,,,0.02444888888888889,9432071415.0,8.8 GB,openneuro,Forced Picture Naming Task,eeg,"[{""val"": 99, ""count"": 16}]","[{""val"": 1000.0, ""count"": 16}]",CC0,doi:10.18112/openneuro.ds004554.v1.0.4,0.0
ds004561,[],,Veillette2023,23,23,1,,,,11.379221527777778,104871731022.0,97.7 GB,openneuro,Illusion of Agency over Electrically-Actuated Movements,eeg,"[{""val"": 64, ""count"": 23}]","[{""val"": 10000.0, ""count"": 23}]",CC0,doi:10.18112/openneuro.ds004561.v1.0.0,2.0
ds004563,[],,Smit2023,40,119,1,Multisensory,Perception,Other,64.68555555555555,108333359388.0,100.9 GB,openneuro,Vicarious touch: overlapping neural patterns between seeing and feeling touch,eeg,"[{""val"": 64, ""count"": 119}]","[{""val"": 2048.0, ""count"": 119}]",CC0,doi:10.18112/openneuro.ds004563.v1.0.1,1.0
ds004572,"[""Kekecs2024""]",author_year,Kekecs2023,52,516,10,Auditory,Other,Healthy,53.24708222222222,46776268551.0,43.6 GB,openneuro,The effects of sham hypnosis techniques,eeg,"[{""val"": 61, ""count"": 516}]","[{""val"": 1000.0, ""count"": 516}]",CC0,doi:10.18112/openneuro.ds004572.v1.3.2,2.0
ds004574,[],,Singh2023_Cross_modal,146,146,1,,,,31.042888888888886,14469987580.0,13.5 GB,openneuro,Cross-modal Oddball Task.,eeg,"[{""val"": 63, ""count"": 116}, {""val"": 64, ""count"": 29}, {""val"": 66, ""count"": 1}]","[{""val"": 500.0, ""count"": 146}]",CC0,doi:10.18112/openneuro.ds004574.v1.0.0,1.0
ds004577,[],,Unit2023,103,130,1,,,,22.973859722222223,684369909.0,652.7 MB,openneuro,Dataset containing resting EEG for a sample of 103 normal infants in the first year of life,eeg,"[{""val"": 19, ""count"": 106}, {""val"": 24, ""count"": 23}, {""val"": 21, ""count"": 1}]","[{""val"": 200.0, ""count"": 130}]",CC0,doi:10.18112/openneuro.ds004577.v1.0.1,3.0
ds004579,[],,Singh2023_Interval_Timing,139,139,1,,,,55.70307777777777,25896737364.0,24.1 GB,openneuro,Interval Timing Task,eeg,"[{""val"": 63, ""count"": 110}, {""val"": 64, ""count"": 28}, {""val"": 66, ""count"": 1}]","[{""val"": 500.0, ""count"": 139}]",CC0,doi:10.18112/openneuro.ds004579.v1.0.0,1.0
ds004580,[],,Singh2023_Simon_conflict,147,147,1,,,,36.514361111111114,17008438192.0,15.8 GB,openneuro,Simon-conflict Task.,eeg,"[{""val"": 63, ""count"": 118}, {""val"": 64, ""count"": 28}, {""val"": 66, ""count"": 1}]","[{""val"": 500.0, ""count"": 147}]",CC0,doi:10.18112/openneuro.ds004580.v1.0.0,1.0
ds004582,[],,Makowski2023_FakeFaceEmo,73,73,1,,,,34.243775722222225,315915939030.0,294.2 GB,openneuro,FakeFaceEmo_data,eeg,"[{""val"": 64, ""count"": 73}]","[{""val"": 10000.0, ""count"": 73}]",CC0,doi:10.18112/openneuro.ds004582.v1.0.0,0.0
ds004584,[],,Singh2023_Rest_eyes,149,149,1,,,,6.641037222222223,3078216426.0,2.9 GB,openneuro,Rest eyes open,eeg,"[{""val"": 63, ""count"": 119}, {""val"": 64, ""count"": 29}, {""val"": 66, ""count"": 1}]","[{""val"": 500.0, ""count"": 149}]",CC0,doi:10.18112/openneuro.ds004584.v1.0.0,2.0
ds004587,[],,Makowski2023_IllusionGameEEG,103,114,1,,,,25.51370786111111,235517890332.0,219.3 GB,openneuro,IllusionGameEEG_data,eeg,"[{""val"": 64, ""count"": 114}]","[{""val"": 10000.0, ""count"": 114}]",CC0,doi:10.18112/openneuro.ds004587.v1.0.0,0.0
ds004588,"[""Neuma""]",canonical,Georgiadis2023,42,42,1,,,,4.957289814814814,560003959.0,534.1 MB,openneuro,Neuma,eeg,"[{""val"": 24, ""count"": 42}]","[{""val"": 300.0, ""count"": 42}]",CC0,doi:10.18112/openneuro.ds004588.v1.2.0,1.0
ds004595,[],,Campbell2023,53,53,1,,,,17.077527777777778,8390886212.0,7.8 GB,openneuro,EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls,eeg,"[{""val"": 66, ""count"": 53}]","[{""val"": 500.0, ""count"": 53}]",CC0,doi:10.18112/openneuro.ds004595.v1.0.0,1.0
ds004598,"[""Moradi2024""]",author_year,Faraz2023,9,20,1,Motor,Memory,Dementia,8.2455,10673023391.0,9.9 GB,openneuro,LFP during linear track in 6-month old TgF344-AD rats,eeg,"[{""val"": 16, ""count"": 20}]","[{""val"": 10000.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds004598.v1.0.0,0.0
ds004602,[],,Clayson2023_Registered,182,546,3,,,,87.17373944444445,79364456510.0,73.9 GB,openneuro,Registered Replication Report of ERN/Pe Psychometrics,eeg,"[{""val"": 129, ""count"": 546}]","[{""val"": 500.0, ""count"": 501}, {""val"": 250.0, ""count"": 45}]",CC0,doi:10.18112/openneuro.ds004602.v1.0.1,5.0
ds004603,"[""VisualContextTrajectory""]",canonical,Lowe2023,37,37,1,,,,30.653045518663195,29391618186.0,27.4 GB,openneuro,Visual Attribute-Specific Contextual Trajectory Paradigm,eeg,"[{""val"": 65, ""count"": 37}]","[{""val"": 1024.0, ""count"": 37}]",CC0,doi:10.18112/openneuro.ds004603.v1.1.0,1.0
ds004621,"[""NenckiSymfonia""]",canonical,Patrycja2023_Nencki,42,167,4,,,,45.42930277777778,83086874697.0,77.4 GB,openneuro,The Nencki-Symfonia EEG/ERP dataset,eeg,"[{""val"": 127, ""count"": 167}]","[{""val"": 1000.0, ""count"": 167}]",CC0,doi:10.18112/openneuro.ds004621.v1.0.4,1.0
ds004624,"[""Mivalt2024"", ""BCI2000_Intracranial""]",canonical,Mivalt2025,3,614,28,Multisensory,Clinical/Intervention,Surgery,,20739089774.0,19.3 GB,openneuro,Intracranial recordings using BCI2000 and the CorTec BrainInterchange,ieeg,"[{""val"": 36, ""count"": 363}, {""val"": 34, ""count"": 234}, {""val"": 39, ""count"": 17}]","[{""val"": 1000.0, ""count"": 614}]",CC0,doi:10.18112/openneuro.ds004624.v2.0.0,0.0
ds004625,[],,Liu2023,32,543,9,Motor,Motor,Healthy,28.581393888888886,67068941232.0,62.5 GB,openneuro,Mind in Motion Young Adults Walking Over Uneven Terrain,eeg,"[{""val"": 284, ""count"": 323}, {""val"": 310, ""count"": 187}, {""val"": 375, ""count"": 33}]","[{""val"": 500.0, ""count"": 543}]",CC0,doi:10.18112/openneuro.ds004625.v1.0.2,0.0
ds004626,[],,Maka2023,52,52,1,,,,21.358625555555555,21336340983.0,19.9 GB,openneuro,Can we dissociate hypervigilance to social threats from altered perceptual decision-making processes in lonely individuals? An exploration with Drift Diffusion Modelling and event-related potentials.,eeg,"[{""val"": 68, ""count"": 52}]","[{""val"": 1000.0, ""count"": 52}]",CC0,doi:10.18112/openneuro.ds004626.v1.0.2,1.0
ds004635,[],,Bagdasarov2023,48,48,1,,,,16.849317222222222,27971375388.0,26.1 GB,openneuro,Gaffrey Lab Infant Microstates Reliability,eeg,"[{""val"": 129, ""count"": 48}]","[{""val"": 1000.0, ""count"": 47}]",CC0,doi:10.18112/openneuro.ds004635.v3.1.0,2.0
ds004642,[],,Dimakopoulos2023_Intraoperative,10,10,1,Other,Other,Surgery,1.069361111111111,1292805561.0,1.2 GB,openneuro,Intraoperative recordings of medianus stimulation with low and high impedance ECoG,ieeg,"[{""val"": 8, ""count"": 7}, {""val"": 9, ""count"": 2}, {""val"": 10, ""count"": 1}]","[{""val"": 20000.0, ""count"": 10}]",CC0,doi:10.18112/openneuro.ds004642.v1.0.1,2.0
ds004657,"[""TX20""]",canonical,Metcalfe2023_Driving,24,119,1,Visual,Decision-making,Healthy,27.205277777777777,46237302253.0,43.1 GB,openneuro,Driving with Autonomous Aids,eeg,"[{""val"": 74, ""count"": 119}]","[{""val"": 1024.0, ""count"": 111}, {""val"": 8192.0, ""count"": 8}]",CC0,doi:10.18112/openneuro.ds004657.v1.0.3,1.0
ds004660,"[""TNO""]",canonical,Johnson2023_TNO,21,42,1,,,,23.79277777777778,7782408262.0,7.2 GB,openneuro,TNO,eeg,"[{""val"": 38, ""count"": 42}]","[{""val"": 512.0, ""count"": 41}, {""val"": 2048.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004660.v1.0.2,1.0
ds004661,"[""ANDI""]",canonical,Johnson2023_ANDI,17,17,1,Multisensory,Attention,Healthy,10.13709201388889,1505576944.0,1.4 GB,openneuro,ANDI,eeg,"[{""val"": 64, ""count"": 17}]","[{""val"": 128.0, ""count"": 17}]",CC0,doi:10.18112/openneuro.ds004661.v1.1.0,0.0
ds004696,[],,Valencia2023,8,8,1,Other,Clinical/Intervention,Epilepsy,9.122338324652778,15212997823.0,14.2 GB,openneuro,HAPwave_bids,ieeg,"[{""val"": 226, ""count"": 1}, {""val"": 201, ""count"": 1}, {""val"": 178, ""count"": 1}, {""val"": 192, ""count"": 1}, {""val"": 194, ""count"": 1}, {""val"": 244, ""count"": 1}, {""val"": 207, ""count"": 1}, {""val"": 256, ""count"": 1}]","[{""val"": 2048.0, ""count"": 8}]",CC0,doi:10.18112/openneuro.ds004696.v1.0.1,1.0
ds004703,[],,Mai2023,10,11,1,Auditory,Memory,Surgery,9.091059027777778,13278807157.0,12.4 GB,openneuro,sEEG Passive listening to natural speech,ieeg,"[{""val"": 148, ""count"": 4}, {""val"": 279, ""count"": 2}, {""val"": 276, ""count"": 2}, {""val"": 277, ""count"": 2}, {""val"": 280, ""count"": 1}]","[{""val"": 1024.0, ""count"": 7}, {""val"": 512.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds004703.v1.1.0,2.0
ds004706,[],,Rudoler2023,34,298,2,Visual,Memory,Healthy,470.60607069227433,1426144562699.0,1.3 TB,openneuro,Spatial memory and non-invasive closed-loop stimulus timing,eeg,"[{""val"": 137, ""count"": 298}]","[{""val"": 2048.0, ""count"": 298}]",CC0,doi:10.18112/openneuro.ds004706.v1.0.0,3.0
ds004718,[],,Momenian2023,51,51,1,,,,21.836041388888887,39765512028.0,37.0 GB,openneuro,Le Petit Prince Hong Kong: Naturalistic fMRI and EEG dataset from older Cantonese speakers,eeg,"[{""val"": 64, ""count"": 51}]","[{""val"": 1000.0, ""count"": 51}]",CC0,doi:10.18112/openneuro.ds004718.v1.1.2,1.0
ds004738,[],,Bahners2023,4,25,2,Other,Other,Other,0.4349166666666667,6587104869.0,6.1 GB,openneuro,sfb_meg_phantom (B04/C01),meg,"[{""val"": 323, ""count"": 13}, {""val"": 160, ""count"": 12}]","[{""val"": 5000.0, ""count"": 25}]",CC0,doi:10.18112/openneuro.ds004738.v1.0.1,1.0
ds004745,[],,Kumaravel2023,6,6,1,Visual,Other,Healthy,1.7556605555555556,253839277.0,242.1 MB,openneuro,8-Channel SSVEP EEG Dataset with Artifact Trials,eeg,"[{""val"": 8, ""count"": 6}]","[{""val"": 1000.0, ""count"": 6}]",CC0,doi:10.18112/openneuro.ds004745.v1.0.1,0.0
ds004752,[],,Dimakopoulos2023_intracranial,15,136,1,,,,0.3022222222222222,10984448479.0,10.2 GB,openneuro,"Dataset of intracranial EEG, scalp EEG and beamforming sources from epilepsy patients performing a verbal working memory task","eeg, ieeg","[{""val"": 64, ""count"": 34}, {""val"": 8, ""count"": 16}, {""val"": 20, ""count"": 15}, {""val"": 21, ""count"": 14}, {""val"": 19, ""count"": 10}, {""val"": 10, ""count"": 7}, {""val"": 46, ""count"": 6}, {""val"": 68, ""count"": 6}, {""val"": 36, ""count"": 6}, {""val"": 23, ""count"": 6}, {""val"": 62, ""count"": 4}, {""val"": 48, ""count"": 4}, {""val"": 32, ""count"": 3}, {""val"": 40, ""count"": 3}, {""val"": 80, ""count"": 2}]","[{""val"": 2000.0, ""count"": 40}, {""val"": 4000.0, ""count"": 40}, {""val"": 200.0, ""count"": 36}, {""val"": 4096.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds004752.v1.0.1,4.0
ds004770,[],,Ueda2023,10,22,1,Visual,Memory,Epilepsy,2.0000166666666668,9325612810.0,8.7 GB,openneuro,iEEG on children during gameplay,ieeg,"[{""val"": 128, ""count"": 14}, {""val"": 105, ""count"": 2}, {""val"": 112, ""count"": 2}, {""val"": 110, ""count"": 2}, {""val"": 113, ""count"": 2}]","[{""val"": 1000.0, ""count"": 22}]",CC0,doi:10.18112/openneuro.ds004770.v1.0.0,2.0
ds004771,[],,Kuo2023,61,61,1,,,,0.02210720486111111,1462175616.0,1.4 GB,openneuro,EEG/ERP data from a Python Reading Task,eeg,"[{""val"": 34, ""count"": 61}]","[{""val"": 256.0, ""count"": 61}]",CC0,doi:10.18112/openneuro.ds004771.v1.0.0,1.0
ds004774,"[""ERDetect"", ""ER_Detect""]",canonical,Boom2023,14,14,2,Other,Clinical/Intervention,Epilepsy,11.947781982421875,26608945134.0,24.8 GB,openneuro,Automatic Evoked Response Detection (ER-Detect) dataset,ieeg,"[{""val"": 133, ""count"": 6}, {""val"": 68, ""count"": 2}, {""val"": 39, ""count"": 2}, {""val"": 130, ""count"": 1}, {""val"": 97, ""count"": 1}, {""val"": 89, ""count"": 1}, {""val"": 65, ""count"": 1}]","[{""val"": 2048.0, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds004774.v1.0.0,
ds004784,[],,Downey2023,1,6,6,,,,0.5408333333333334,1084178641.0,1.0 GB,openneuro,"Phantom EEG Dataset with Motion, Muscle, and Eye Artifacts and Example Scripts",eeg,"[{""val"": 264, ""count"": 6}]","[{""val"": 512.0, ""count"": 6}]",CC0,doi:10.18112/openneuro.ds004784.v1.0.4,1.0
ds004785,[],,Boebinger2023,17,17,1,,,,0.01888888888888889,368223688.0,351.2 MB,openneuro,EEG data for paper titled - Precise cortical contributions to feedback sensorimotor control during reactive balance,eeg,"[{""val"": 32, ""count"": 17}]","[{""val"": 500.0, ""count"": 17}]",CC0,doi:10.18112/openneuro.ds004785.v1.0.1,1.0
ds004789,[],,Herrema2023_Delayed_Free_Recall,273,983,1,Visual,Memory,Epilepsy,776.4940177977268,618808962711.0,576.3 GB,openneuro,Delayed Free Recall of Word Lists,ieeg,"[{""val"": 126, ""count"": 87}, {""val"": 108, ""count"": 32}, {""val"": 112, ""count"": 32}, {""val"": 110, ""count"": 31}, {""val"": 88, ""count"": 29}, {""val"": 120, ""count"": 28}, {""val"": 128, ""count"": 28}, {""val"": 127, ""count"": 24}, {""val"": 116, ""count"": 22}, {""val"": 124, ""count"": 21}, {""val"": 196, ""count"": 20}, {""val"": 109, ""count"": 19}, {""val"": 111, ""count"": 18}, {""val"": 106, ""count"": 17}, {""val"": 100, ""count"": 17}, {""val"": 113, ""count"": 16}, {""val"": 125, ""count"": 15}, {""val"": 86, ""count"": 14}, {""val"": 107, ""count"": 13}, {""val"": 64, ""count"": 13}, {""val"": 60, ""count"": 13}, {""val"": 158, ""count"": 12}, {""val"": 118, ""count"": 12}, {""val"": 68, ""count"": 11}, {""val"": 104, ""count"": 11}, {""val"": 178, ""count"": 10}, {""val"": 76, ""count"": 10}, {""val"": 180, ""count"": 10}, {""val"": 122, ""count"": 10}, {""val"": 121, ""count"": 10}, {""val"": 102, ""count"": 9}, {""val"": 80, ""count"": 9}, {""val"": 142, ""count"": 9}, {""val"": 56, ""count"": 9}, {""val"": 153, ""count"": 8}, {""val"": 97, ""count"": 8}, {""val"": 140, ""count"": 8}, {""val"": 75, ""count"": 8}, {""val"": 188, ""count"": 7}, {""val"": 114, ""count"": 7}, {""val"": 62, ""count"": 7}, {""val"": 85, ""count"": 7}, {""val"": 146, ""count"": 7}, {""val"": 172, ""count"": 7}, {""val"": 130, ""count"": 7}, {""val"": 148, ""count"": 7}, {""val"": 90, ""count"": 7}, {""val"": 83, ""count"": 7}, {""val"": 92, ""count"": 6}, {""val"": 72, ""count"": 6}, {""val"": 162, ""count"": 6}, {""val"": 168, ""count"": 6}, {""val"": 139, ""count"": 6}, {""val"": 173, ""count"": 6}, {""val"": 70, ""count"": 6}, {""val"": 134, ""count"": 6}, {""val"": 78, ""count"": 6}, {""val"": 96, ""count"": 5}, {""val"": 74, ""count"": 5}, {""val"": 206, ""count"": 5}, {""val"": 93, ""count"": 5}, {""val"": 165, ""count"": 5}, {""val"": 141, ""count"": 5}, {""val"": 160, ""count"": 5}, {""val"": 84, ""count"": 4}, {""val"": 161, ""count"": 4}, {""val"": 203, ""count"": 4}, {""val"": 119, ""count"": 4}, {""val"": 136, ""count"": 4}, {""val"": 177, ""count"": 4}, {""val"": 224, ""count"": 4}, {""val"": 54, ""count"": 4}, {""val"": 200, ""count"": 4}, {""val"": 46, ""count"": 4}, {""val"": 123, ""count"": 4}, {""val"": 208, ""count"": 3}, {""val"": 186, ""count"": 3}, {""val"": 50, ""count"": 3}, {""val"": 176, ""count"": 3}, {""val"": 37, ""count"": 3}, {""val"": 212, ""count"": 3}, {""val"": 138, ""count"": 3}, {""val"": 59, ""count"": 3}, {""val"": 94, ""count"": 3}, {""val"": 99, ""count"": 3}, {""val"": 154, ""count"": 3}, {""val"": 103, ""count"": 3}, {""val"": 152, ""count"": 3}, {""val"": 166, ""count"": 3}, {""val"": 133, ""count"": 3}, {""val"": 69, ""count"": 3}, {""val"": 151, ""count"": 2}, {""val"": 170, ""count"": 2}, {""val"": 95, ""count"": 2}, {""val"": 58, ""count"": 2}, {""val"": 55, ""count"": 2}, {""val"": 184, ""count"": 2}, {""val"": 218, ""count"": 2}, {""val"": 213, ""count"": 2}, {""val"": 36, ""count"": 2}, {""val"": 156, ""count"": 2}, {""val"": 52, ""count"": 2}, {""val"": 67, ""count"": 2}, {""val"": 179, ""count"": 2}, {""val"": 87, ""count"": 2}, {""val"": 182, ""count"": 2}, {""val"": 105, ""count"": 2}, {""val"": 149, ""count"": 2}, {""val"": 43, ""count"": 2}, {""val"": 26, ""count"": 2}, {""val"": 77, ""count"": 1}, {""val"": 53, ""count"": 1}, {""val"": 101, ""count"": 1}, {""val"": 190, ""count"": 1}, {""val"": 16, ""count"": 1}, {""val"": 129, ""count"": 1}, {""val"": 98, ""count"": 1}, {""val"": 202, ""count"": 1}, {""val"": 14, ""count"": 1}, {""val"": 209, ""count"": 1}, {""val"": 216, ""count"": 1}, {""val"": 48, ""count"": 1}, {""val"": 195, ""count"": 1}, {""val"": 175, ""count"": 1}, {""val"": 229, ""count"": 1}, {""val"": 73, ""count"": 1}, {""val"": 65, ""count"": 1}, {""val"": 215, ""count"": 1}, {""val"": 131, ""count"": 1}, {""val"": 38, ""count"": 1}, {""val"": 63, ""count"": 1}]","[{""val"": 1000.0, ""count"": 785}, {""val"": 500.0, ""count"": 119}, {""val"": 1600.0, ""count"": 32}, {""val"": 999.0, ""count"": 19}, {""val"": 499.7071, ""count"": 16}, {""val"": 2000.0, ""count"": 6}, {""val"": 1024.0, ""count"": 4}, {""val"": 512.0, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds004789.v3.1.0,3.0
ds004796,"[""PEARLNeuro""]",canonical,Patrycja2023_Polish,79,235,3,,,,43.99488333333333,257896989782.0,240.2 GB,openneuro,"A Polish Electroencephalography, Alzheimer’s Risk-genes, Lifestyle and Neuroimaging (PEARL-Neuro) Database",eeg,"[{""val"": 127, ""count"": 235}]","[{""val"": 1000.0, ""count"": 235}]",CC0,doi:10.18112/openneuro.ds004796.v1.1.0,1.0
ds004802,[],,Bathelt2023,39,79,1,,,,40.855,10810016249.0,10.1 GB,openneuro,Pilot data for Loneliness in the Brain: Distinguishing Between Hypersensitivity and Hyperalertness,eeg,"[{""val"": 69, ""count"": 38}]","[{""val"": 512.0, ""count"": 27}, {""val"": 2048.0, ""count"": 11}]",CC0,doi:10.18112/openneuro.ds004802.v1.0.0,1.0
ds004809,"[""catFR_Categorized_Free_Recall"", ""CatFR""]",canonical,Herrema2023_Categorized_Free_Recall,252,889,1,Visual,Memory,Epilepsy,575.3024328526149,512365835522.0,477.2 GB,openneuro,Categorized Free Recall: Delayed Free Recall of Word Lists Organized by Semantic Categories,ieeg,"[{""val"": 126, ""count"": 70}, {""val"": 124, ""count"": 30}, {""val"": 108, ""count"": 26}, {""val"": 125, ""count"": 20}, {""val"": 128, ""count"": 19}, {""val"": 139, ""count"": 19}, {""val"": 88, ""count"": 17}, {""val"": 127, ""count"": 16}, {""val"": 120, ""count"": 16}, {""val"": 131, ""count"": 15}, {""val"": 145, ""count"": 15}, {""val"": 148, ""count"": 15}, {""val"": 116, ""count"": 15}, {""val"": 64, ""count"": 14}, {""val"": 196, ""count"": 14}, {""val"": 112, ""count"": 14}, {""val"": 110, ""count"": 13}, {""val"": 142, ""count"": 13}, {""val"": 179, ""count"": 13}, {""val"": 118, ""count"": 12}, {""val"": 155, ""count"": 12}, {""val"": 133, ""count"": 11}, {""val"": 114, ""count"": 11}, {""val"": 121, ""count"": 11}, {""val"": 251, ""count"": 11}, {""val"": 159, ""count"": 11}, {""val"": 90, ""count"": 11}, {""val"": 178, ""count"": 10}, {""val"": 113, ""count"": 10}, {""val"": 186, ""count"": 10}, {""val"": 94, ""count"": 10}, {""val"": 92, ""count"": 10}, {""val"": 158, ""count"": 9}, {""val"": 115, ""count"": 9}, {""val"": 105, ""count"": 9}, {""val"": 152, ""count"": 9}, {""val"": 198, ""count"": 9}, {""val"": 200, ""count"": 8}, {""val"": 183, ""count"": 8}, {""val"": 156, ""count"": 8}, {""val"": 247, ""count"": 8}, {""val"": 104, ""count"": 8}, {""val"": 106, ""count"": 7}, {""val"": 166, ""count"": 7}, {""val"": 122, ""count"": 7}, {""val"": 98, ""count"": 7}, {""val"": 68, ""count"": 7}, {""val"": 212, ""count"": 7}, {""val"": 240, ""count"": 6}, {""val"": 241, ""count"": 6}, {""val"": 100, ""count"": 6}, {""val"": 109, ""count"": 6}, {""val"": 76, ""count"": 6}, {""val"": 78, ""count"": 6}, {""val"": 184, ""count"": 6}, {""val"": 150, ""count"": 6}, {""val"": 154, ""count"": 5}, {""val"": 56, ""count"": 5}, {""val"": 208, ""count"": 5}, {""val"": 165, ""count"": 5}, {""val"": 168, ""count"": 5}, {""val"": 250, ""count"": 5}, {""val"": 224, ""count"": 4}, {""val"": 141, ""count"": 4}, {""val"": 189, ""count"": 4}, {""val"": 164, ""count"": 4}, {""val"": 192, ""count"": 4}, {""val"": 180, ""count"": 4}, {""val"": 97, ""count"": 4}, {""val"": 72, ""count"": 4}, {""val"": 70, ""count"": 4}, {""val"": 89, ""count"": 4}, {""val"": 238, ""count"": 4}, {""val"": 185, ""count"": 4}, {""val"": 173, ""count"": 4}, {""val"": 219, ""count"": 4}, {""val"": 175, ""count"": 4}, {""val"": 134, ""count"": 4}, {""val"": 188, ""count"": 4}, {""val"": 83, ""count"": 3}, {""val"": 160, ""count"": 3}, {""val"": 167, ""count"": 3}, {""val"": 140, ""count"": 3}, {""val"": 209, ""count"": 3}, {""val"": 95, ""count"": 3}, {""val"": 220, ""count"": 3}, {""val"": 130, ""count"": 3}, {""val"": 162, ""count"": 3}, {""val"": 46, ""count"": 3}, {""val"": 60, ""count"": 3}, {""val"": 229, ""count"": 3}, {""val"": 207, ""count"": 3}, {""val"": 123, ""count"": 2}, {""val"": 119, ""count"": 2}, {""val"": 169, ""count"": 2}, {""val"": 203, ""count"": 2}, {""val"": 161, ""count"": 2}, {""val"": 84, ""count"": 2}, {""val"": 177, ""count"": 2}, {""val"": 151, ""count"": 2}, {""val"": 172, ""count"": 2}, {""val"": 93, ""count"": 2}, {""val"": 53, ""count"": 2}, {""val"": 96, ""count"": 2}, {""val"": 132, ""count"": 2}, {""val"": 67, ""count"": 2}, {""val"": 176, ""count"": 2}, {""val"": 193, ""count"": 2}, {""val"": 187, ""count"": 2}, {""val"": 80, ""count"": 1}, {""val"": 146, ""count"": 1}, {""val"": 14, ""count"": 1}, {""val"": 136, ""count"": 1}, {""val"": 52, ""count"": 1}, {""val"": 16, ""count"": 1}, {""val"": 86, ""count"": 1}, {""val"": 239, ""count"": 1}, {""val"": 75, ""count"": 1}, {""val"": 182, ""count"": 1}, {""val"": 102, ""count"": 1}, {""val"": 85, ""count"": 1}, {""val"": 63, ""count"": 1}, {""val"": 206, ""count"": 1}, {""val"": 50, ""count"": 1}, {""val"": 213, ""count"": 1}, {""val"": 111, ""count"": 1}, {""val"": 99, ""count"": 1}, {""val"": 62, ""count"": 1}, {""val"": 37, ""count"": 1}, {""val"": 163, ""count"": 1}, {""val"": 243, ""count"": 1}, {""val"": 36, ""count"": 1}, {""val"": 107, ""count"": 1}, {""val"": 153, ""count"": 1}, {""val"": 143, ""count"": 1}, {""val"": 26, ""count"": 1}, {""val"": 202, ""count"": 1}, {""val"": 218, ""count"": 1}]","[{""val"": 1000.0, ""count"": 766}, {""val"": 500.0, ""count"": 93}, {""val"": 1600.0, ""count"": 10}, {""val"": 999.0, ""count"": 8}, {""val"": 1023.999, ""count"": 6}, {""val"": 1024.0, ""count"": 4}, {""val"": 499.7071, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds004809.v2.2.0,1.0
ds004816,[],,Grootswagers2023_E1,20,20,1,,,,10.713711111111111,9731821453.0,9.1 GB,openneuro,EEG-attention-rsvp-exp1,eeg,"[{""val"": 63, ""count"": 20}]","[{""val"": 1000.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds004816.v1.0.0,0.0
ds004817,[],,Grootswagers2023_E2,20,20,1,,,,11.900994444444445,10808487309.0,10.1 GB,openneuro,EEG-attention-rsvp-exp2,eeg,"[{""val"": 63, ""count"": 20}]","[{""val"": 1000.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds004817.v1.0.0,0.0
ds004819,[],,Lee2023,1,8,1,Other,Clinical/Intervention,Surgery,0.052222222222222225,722127657.0,688.7 MB,openneuro,"Flexible, Scalable, High Channel Count Stereo-Electrode for Recording in the Human Brain",ieeg,"[{""val"": 64, ""count"": 8}]","[{""val"": 30000.0, ""count"": 8}]",CC0,doi:10.18112/openneuro.ds004819.v1.0.0,1.0
ds004830,"[""Ning2024""]",author_year,Ning2023,12,14,1,Visual,Attention,Healthy,,1321472833.0,1.2 GB,openneuro,Spatial Attention Decoding using fNIRS During Complex Scene Analysis,fnirs,"[{""val"": 72, ""count"": 27}, {""val"": 84, ""count"": 6}]","[{""val"": 50.0, ""count"": 32}, {""val"": 50.00000000000001, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004830.v2.0.0,
ds004837,[],,LopezCaballero2023,60,106,1,Auditory,Perception,Schizophrenia/Psychosis,,128779377455.0,119.9 GB,openneuro,Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis,meg,[],"[{""val"": 3000, ""count"": 98}, {""val"": 1000, ""count"": 8}]",CC0,doi:10.18112/openneuro.ds004837.v1.0.2,0.0
ds004840,[],,CordobaSilva2023,9,51,3,,,,10.935833333333333,628618633.0,599.5 MB,openneuro,"Dataset of electrophysiological signals (EEG, ECG, EMG) during Music therapy with adult burn patients in the Intensive Care Unit.",eeg,"[{""val"": 10, ""count"": 45}, {""val"": 9, ""count"": 3}, {""val"": 8, ""count"": 3}]","[{""val"": 1024.0, ""count"": 33}, {""val"": 512.0, ""count"": 15}, {""val"": 256.0, ""count"": 3}]",CC0,doi:10.18112/openneuro.ds004840.v1.0.1,1.0
ds004841,"[""TX14""]",canonical,Larkin2023_TX14,20,147,1,Visual,Attention,Healthy,28.445555555555554,7846933953.0,7.3 GB,openneuro,TX14,eeg,"[{""val"": 70, ""count"": 147}]","[{""val"": 256.0, ""count"": 147}]",CC0,doi:10.18112/openneuro.ds004841.v1.0.1,0.0
ds004842,"[""TX15""]",canonical,Larkin2023_TX15,14,102,1,,,,20.165277777777778,5589053822.0,5.2 GB,openneuro,TX15,eeg,"[{""val"": 70, ""count"": 94}, {""val"": 72, ""count"": 8}]","[{""val"": 256.0, ""count"": 102}]",CC0,doi:10.18112/openneuro.ds004842.v1.0.0,0.0
ds004843,[],,Johnson2023_T16,14,92,1,Visual,Attention,Healthy,28.9975,8229205347.0,7.7 GB,openneuro,T16,eeg,"[{""val"": 70, ""count"": 92}]","[{""val"": 256.0, ""count"": 92}]",CC0,doi:10.18112/openneuro.ds004843.v1.0.0,0.0
ds004844,[],,Metcalfe2023_T22,17,68,1,Visual,Decision-making,Healthy,21.252222222222223,23976121518.0,22.3 GB,openneuro,T22,eeg,"[{""val"": 72, ""count"": 68}]","[{""val"": 1024.0, ""count"": 68}]",CC0,doi:10.18112/openneuro.ds004844.v1.0.0,0.0
ds004849,"[""STRONG""]",canonical,Johnson2023_STRONG,1,1,1,Unknown,Memory,Unknown,0.5350499131944444,83056636.0,79.2 MB,openneuro,STRONG,eeg,"[{""val"": 64, ""count"": 1}]","[{""val"": 128.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004849.v1.0.0,0.0
ds004850,"[""Johnson2024""]",author_year,Johnson2023_ODE,1,1,1,Unknown,Memory,Unknown,0.5350499131944444,83056630.0,79.2 MB,openneuro,ODE,eeg,"[{""val"": 64, ""count"": 1}]","[{""val"": 128.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004850.v1.0.0,0.0
ds004851,"[""HID""]",canonical,Johnson2023_HID,66,66,1,Unknown,Unknown,Unknown,18.58285617404514,60057550495.0,55.9 GB,openneuro,HID,eeg,"[{""val"": 72, ""count"": 66}]","[{""val"": 2048.0, ""count"": 66}]",CC0,doi:10.18112/openneuro.ds004851.v2.1.0,0.0
ds004852,"[""Johnson2025""]",author_year,Johnson2023_InsurgentCivilian,1,1,1,Unknown,Memory,Unknown,0.5350499131944444,83056658.0,79.2 MB,openneuro,InsurgentCivilian,eeg,"[{""val"": 64, ""count"": 1}]","[{""val"": 128.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004852.v1.0.0,0.0
ds004853,[],,Johnson2023_TX17,1,1,1,Unknown,Memory,Unknown,0.5350499131944444,83056632.0,79.2 MB,openneuro,TX17,eeg,"[{""val"": 64, ""count"": 1}]","[{""val"": 128.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004853.v1.0.0,0.0
ds004854,"[""TX18""]",canonical,Johnson2023_TX18,1,1,1,Unknown,Memory,Unknown,0.5350499131944444,83056632.0,79.2 MB,openneuro,TX18,eeg,"[{""val"": 64, ""count"": 1}]","[{""val"": 128.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004854.v1.0.0,0.0
ds004855,[],,Johnson2023_FT,1,1,1,Unknown,Memory,Unknown,0.5350499131944444,83056628.0,79.2 MB,openneuro,FT,eeg,"[{""val"": 64, ""count"": 1}]","[{""val"": 128.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004855.v1.0.0,0.0
ds004859,"[""Sakakura2024""]",author_year,Sakakura2023_children_Stroop,7,9,1,Visual,Attention,Unknown,2.669722222222222,2435511071.0,2.3 GB,openneuro,iEEG on children during Stroop task,ieeg,"[{""val"": 128, ""count"": 8}, {""val"": 108, ""count"": 1}]","[{""val"": 1000.0, ""count"": 9}]",CC0,doi:10.18112/openneuro.ds004859.v1.0.0,0.0
ds004860,[],,Schwartz2023,31,31,1,,,,16.364166666666666,4065631774.0,3.8 GB,openneuro,Investigating the cognitive conflict triggered by moral judgment of accidental harm : an event-related potentials study,eeg,"[{""val"": 36, ""count"": 31}]","[{""val"": 512.0, ""count"": 30}, {""val"": 2048.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004860.v1.0.0,1.0
ds004865,"[""pyFR""]",canonical,Herrema2023_pyFR_Delayed_Free,42,172,1,Visual,Memory,Surgery,180.6291955494362,104999471870.0,97.8 GB,openneuro,"pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study",ieeg,"[{""val"": 100, ""count"": 7}, {""val"": 80, ""count"": 5}, {""val"": 74, ""count"": 5}, {""val"": 131, ""count"": 5}, {""val"": 46, ""count"": 4}, {""val"": 108, ""count"": 4}, {""val"": 62, ""count"": 4}, {""val"": 110, ""count"": 4}, {""val"": 54, ""count"": 4}, {""val"": 85, ""count"": 4}, {""val"": 86, ""count"": 4}, {""val"": 53, ""count"": 4}, {""val"": 32, ""count"": 3}, {""val"": 116, ""count"": 3}, {""val"": 47, ""count"": 3}, {""val"": 150, ""count"": 3}, {""val"": 121, ""count"": 3}, {""val"": 42, ""count"": 3}, {""val"": 55, ""count"": 3}, {""val"": 75, ""count"": 3}, {""val"": 78, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 109, ""count"": 3}, {""val"": 27, ""count"": 3}, {""val"": 82, ""count"": 3}, {""val"": 91, ""count"": 3}, {""val"": 72, ""count"": 3}, {""val"": 88, ""count"": 3}, {""val"": 105, ""count"": 3}, {""val"": 168, ""count"": 3}, {""val"": 48, ""count"": 3}, {""val"": 123, ""count"": 3}, {""val"": 96, ""count"": 3}, {""val"": 70, ""count"": 3}, {""val"": 104, ""count"": 3}, {""val"": 130, ""count"": 2}, {""val"": 63, ""count"": 2}, {""val"": 126, ""count"": 2}, {""val"": 68, ""count"": 2}, {""val"": 57, ""count"": 2}, {""val"": 52, ""count"": 2}, {""val"": 36, ""count"": 2}, {""val"": 102, ""count"": 2}, {""val"": 124, ""count"": 2}, {""val"": 76, ""count"": 2}, {""val"": 111, ""count"": 2}, {""val"": 58, ""count"": 2}, {""val"": 149, ""count"": 2}, {""val"": 144, ""count"": 2}, {""val"": 87, ""count"": 2}, {""val"": 119, ""count"": 2}, {""val"": 153, ""count"": 2}, {""val"": 142, ""count"": 2}, {""val"": 187, ""count"": 1}, {""val"": 95, ""count"": 1}, {""val"": 81, ""count"": 1}, {""val"": 90, ""count"": 1}, {""val"": 56, ""count"": 1}, {""val"": 94, ""count"": 1}, {""val"": 98, ""count"": 1}, {""val"": 160, ""count"": 1}, {""val"": 203, ""count"": 1}, {""val"": 120, ""count"": 1}, {""val"": 101, ""count"": 1}, {""val"": 97, ""count"": 1}, {""val"": 64, ""count"": 1}]","[{""val"": 1000.0, ""count"": 102}, {""val"": 512.0, ""count"": 40}, {""val"": 2000.0, ""count"": 16}, {""val"": 400.0, ""count"": 8}, {""val"": 499.7071, ""count"": 6}]",CC0,doi:10.18112/openneuro.ds004865.v2.0.1,0.0
ds004883,[],,Clayson2023_Registerd,172,516,3,,,,139.97186555555555,131858855109.0,122.8 GB,openneuro,Registerd Report of ERN During Three Versions of a Flanker Task,eeg,"[{""val"": 129, ""count"": 516}]","[{""val"": 500.0, ""count"": 516}]",CC0,doi:10.18112/openneuro.ds004883.v1.0.0,3.0
ds004902,[],,Xiang2023,71,218,2,,,,18.118427777777775,8898598868.0,8.3 GB,openneuro,A Resting-state EEG Dataset for Sleep Deprivation,eeg,"[{""val"": 61, ""count"": 218}]","[{""val"": 500.0, ""count"": 215}, {""val"": 5000.0, ""count"": 3}]",CC0,doi:10.18112/openneuro.ds004902.v1.0.8,3.0
ds004917,[],,FigueroaVargas2024,24,24,1,,,,14.579594444444444,40213447001.0,37.5 GB,openneuro,Probability Decision-making Task with ambiguity,eeg,"[{""val"": 66, ""count"": 24}]","[{""val"": 5000.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds004917.v1.0.1,0.0
ds004929,[],,Gao2024,12,36,1,Motor,Motor,Unknown,,317141546.0,302.4 MB,openneuro,BallSqueezingHD,fnirs,"[{""val"": 200, ""count"": 36}]","[{""val"": 8.719308035714286, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds004929.v1.0.0,
ds004940,[],,Toffolo2024,22,48,2,Auditory,Attention,Healthy,36.86361111111111,127252523354.0,118.5 GB,openneuro,Neurophysiological measures of covert semantic processing in neurotypical adolescents actively ignoring spoken sentence inputs: A high-density event-related potential (ERP) study.,eeg,"[{""val"": 128, ""count"": 48}]","[{""val"": 512.0, ""count"": 48}]",CC0,doi:10.18112/openneuro.ds004940.v1.0.1,
ds004942,[],,Kieffaber2024,62,62,1,,,,28.282224444444445,26899933059.0,25.1 GB,openneuro,SpatialMemory,eeg,"[{""val"": 65, ""count"": 62}]","[{""val"": 1000.0, ""count"": 62}]",CC0,doi:10.18112/openneuro.ds004942.v1.0.0,1.0
ds004944,"[""BCI2000_intraop""]",canonical,Costa2024,22,44,1,Other,Clinical/Intervention,Epilepsy,3.0518518055555552,473061643.0,451.1 MB,openneuro,Dataset of BCI2000-compatible intraoperative ECoG with neuromorphic encoding,ieeg,"[{""val"": 3, ""count"": 10}, {""val"": 6, ""count"": 6}, {""val"": 5, ""count"": 6}, {""val"": 4, ""count"": 5}, {""val"": 23, ""count"": 3}, {""val"": 20, ""count"": 2}, {""val"": 19, ""count"": 2}, {""val"": 25, ""count"": 1}, {""val"": 22, ""count"": 1}, {""val"": 10, ""count"": 1}, {""val"": 15, ""count"": 1}, {""val"": 28, ""count"": 1}, {""val"": 27, ""count"": 1}, {""val"": 2, ""count"": 1}, {""val"": 17, ""count"": 1}, {""val"": 11, ""count"": 1}, {""val"": 21, ""count"": 1}]","[{""val"": 2000.0, ""count"": 44}]",CC0,doi:10.18112/openneuro.ds004944.v1.1.0,1.0
ds004951,"[""Haupt2025""]",author_year,Haupt2024_Braille,11,23,1,Tactile,Learning,Other,25.926832500000003,23627351784.0,22.0 GB,openneuro,Braille letters - EEG,eeg,"[{""val"": 64, ""count"": 13}, {""val"": 63, ""count"": 10}]","[{""val"": 1000.0, ""count"": 23}]",CC0,doi:10.18112/openneuro.ds004951.v1.0.0,1.0
ds004952,[],,Mou2024,10,245,1,,,,121.74074361111111,222393779876.0,207.1 GB,openneuro,ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding,eeg,"[{""val"": 128, ""count"": 245}]","[{""val"": 1000.0, ""count"": 245}]",CC0,doi:10.18112/openneuro.ds004952.v1.2.2,2.0
ds004973,[],,Zhang2024_driving_risk_cognition,20,222,12,Visual,Attention,Healthy,50.14391111111111,2486210305.0,2.3 GB,openneuro,An fNIRS dataset for driving risk cognition of passengers in highly automated driving scenarios,fnirs,"[{""val"": 16, ""count"": 222}]","[{""val"": 50.0, ""count"": 222}]",CC0,doi:10.18112/openneuro.ds004973.v1.0.1,
ds004977,"[""CARLA""]",canonical,Huang2024,4,6,1,Other,Other,Epilepsy,0.8750833333333332,1604950022.0,1.5 GB,openneuro,CARLA: Adjusted common average referencing for cortico-cortical evoked potential data,ieeg,"[{""val"": 273, ""count"": 4}, {""val"": 152, ""count"": 1}, {""val"": 232, ""count"": 1}]","[{""val"": 4800.0, ""count"": 6}]",CC0,doi:10.18112/openneuro.ds004977.v1.2.0,2.0
ds004980,[],,Wang2024_architectural_affordances,17,17,1,,,,36.846338890955984,16989514308.0,15.8 GB,openneuro,EEG data set for a architectural affordances task,eeg,"[{""val"": 64, ""count"": 17}]","[{""val"": 500.0, ""count"": 4}, {""val"": 499.9914353, ""count"": 1}, {""val"": 499.9919367, ""count"": 1}, {""val"": 499.9923017, ""count"": 1}, {""val"": 499.9923795, ""count"": 1}, {""val"": 499.9917272, ""count"": 1}, {""val"": 499.9914553, ""count"": 1}, {""val"": 499.9919292, ""count"": 1}, {""val"": 499.9917286, ""count"": 1}, {""val"": 499.9911824, ""count"": 1}, {""val"": 499.9912809, ""count"": 1}, {""val"": 499.9917378, ""count"": 1}, {""val"": 499.991385, ""count"": 1}, {""val"": 499.9915179, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004980.v1.0.0,1.0
ds004993,"[""WIRED_ICM""]",canonical,Hamilton2024,3,3,3,Auditory,Perception,Epilepsy,0.22991739908854167,319927247.0,305.1 MB,openneuro,"WIRED ICM Sample Dataset - Workshop on Intracranial Recordings in Humans, Epilepsy, DBS",ieeg,"[{""val"": 148, ""count"": 1}, {""val"": 106, ""count"": 1}, {""val"": 160, ""count"": 1}]","[{""val"": 512.0, ""count"": 2}, {""val"": 2048.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds004993.v1.1.2,0.0
ds004995,"[""Moerel2023""]",author_year,Moerel2024,20,20,1,Visual,Perception,Healthy,16.189522222222223,29637642698.0,27.6 GB,openneuro,The Time-Course of Food Representation in the Human Brain,eeg,"[{""val"": 127, ""count"": 20}]","[{""val"": 1000.0, ""count"": 20}]",CC0,doi:10.18112/openneuro.ds004995.v1.0.2,1.0
ds004998,[],,Rassoulou2024,20,145,6,Motor,Motor,Parkinson's,10.766654722222222,173691728283.0,161.8 GB,openneuro,Exploring the electrophysiology of Parkinson's disease - magnetoencephalography combined with deep brain recordings from the subthalamic nucleus.,meg,"[{""val"": 323, ""count"": 122}, {""val"": 326, ""count"": 6}, {""val"": 333, ""count"": 6}, {""val"": 324, ""count"": 6}, {""val"": 347, ""count"": 4}, {""val"": 319, ""count"": 1}]","[{""val"": 2000.0, ""count"": 85}]",CC0,doi:10.18112/openneuro.ds004998.v1.2.2,1.0
ds005007,"[""Kitazawa2025""]",author_year,Kitazawa2024,40,42,1,Auditory,Other,Healthy,9.423494444444446,8896470075.0,8.3 GB,openneuro,Auditory naming task with questions that begin or end with a wh-interrogative,ieeg,"[{""val"": 100, ""count"": 3}, {""val"": 74, ""count"": 2}, {""val"": 78, ""count"": 2}, {""val"": 156, ""count"": 2}, {""val"": 86, ""count"": 2}, {""val"": 58, ""count"": 2}, {""val"": 66, ""count"": 2}, {""val"": 116, ""count"": 2}, {""val"": 82, ""count"": 2}, {""val"": 122, ""count"": 1}, {""val"": 127, ""count"": 1}, {""val"": 155, ""count"": 1}, {""val"": 68, ""count"": 1}, {""val"": 94, ""count"": 1}, {""val"": 128, ""count"": 1}, {""val"": 140, ""count"": 1}, {""val"": 154, ""count"": 1}, {""val"": 138, ""count"": 1}, {""val"": 142, ""count"": 1}, {""val"": 91, ""count"": 1}, {""val"": 72, ""count"": 1}, {""val"": 102, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 137, ""count"": 1}, {""val"": 51, ""count"": 1}, {""val"": 120, ""count"": 1}, {""val"": 163, ""count"": 1}, {""val"": 48, ""count"": 1}, {""val"": 114, ""count"": 1}, {""val"": 129, ""count"": 1}, {""val"": 184, ""count"": 1}, {""val"": 88, ""count"": 1}]","[{""val"": 1000.0, ""count"": 42}]",CC0,doi:10.18112/openneuro.ds005007.v1.0.0,0.0
ds005021,[],,Williams2024,36,36,1,,,,43.06305555555556,51007318536.0,47.5 GB,openneuro,Tilt Illusion by Phase,eeg,"[{""val"": 72, ""count"": 36}]","[{""val"": 1024.0, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds005021.v1.2.1,0.0
ds005028,"[""Chandravadia2022""]",author_year,Chandravadia2024,11,105,3,Visual,Attention,Unknown,0.029166666666666667,442568172.0,422.1 MB,openneuro,Comparing P300 Flashing paradigms in online typing with language models,eeg,"[{""val"": 32, ""count"": 1}]",[],CC0,doi:10.18112/openneuro.ds005028.v1.0.0,0.0
ds005034,[],,Pavlov2024_effect_theta_tACS,25,100,2,,,,34.91862888888889,65885314989.0,61.4 GB,openneuro,The effect of theta tACS on working memory,eeg,"[{""val"": 129, ""count"": 100}]","[{""val"": 1000.0, ""count"": 100}]",CC0,doi:10.18112/openneuro.ds005034.v1.0.1,1.0
ds005048,[],,Lahijanian2024,35,35,1,,,,5.2027777777777775,373200390.0,355.9 MB,openneuro,40Hz Auditory Entrainment,eeg,"[{""val"": 19, ""count"": 35}]","[{""val"": 250.0, ""count"": 35}]",CC0,doi:10.18112/openneuro.ds005048.v1.0.1,1.0
ds005059,"[""PAL""]",canonical,Herrema2024_Paired,69,282,1,Visual,Memory,Epilepsy,261.31572875603473,179631838657.0,167.3 GB,openneuro,Paired Associates Learning: Memory for Word Pairs in Cued Recall,ieeg,"[{""val"": 112, ""count"": 22}, {""val"": 126, ""count"": 15}, {""val"": 85, ""count"": 11}, {""val"": 110, ""count"": 10}, {""val"": 128, ""count"": 10}, {""val"": 104, ""count"": 9}, {""val"": 88, ""count"": 9}, {""val"": 100, ""count"": 9}, {""val"": 72, ""count"": 8}, {""val"": 64, ""count"": 8}, {""val"": 186, ""count"": 8}, {""val"": 102, ""count"": 7}, {""val"": 116, ""count"": 7}, {""val"": 121, ""count"": 7}, {""val"": 92, ""count"": 6}, {""val"": 142, ""count"": 6}, {""val"": 119, ""count"": 5}, {""val"": 97, ""count"": 5}, {""val"": 95, ""count"": 5}, {""val"": 94, ""count"": 5}, {""val"": 106, ""count"": 4}, {""val"": 140, ""count"": 4}, {""val"": 124, ""count"": 4}, {""val"": 96, ""count"": 4}, {""val"": 123, ""count"": 4}, {""val"": 139, ""count"": 4}, {""val"": 86, ""count"": 4}, {""val"": 130, ""count"": 4}, {""val"": 68, ""count"": 4}, {""val"": 87, ""count"": 3}, {""val"": 107, ""count"": 3}, {""val"": 188, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 120, ""count"": 3}, {""val"": 58, ""count"": 3}, {""val"": 74, ""count"": 3}, {""val"": 114, ""count"": 3}, {""val"": 83, ""count"": 3}, {""val"": 108, ""count"": 3}, {""val"": 55, ""count"": 3}, {""val"": 80, ""count"": 3}, {""val"": 117, ""count"": 3}, {""val"": 173, ""count"": 3}, {""val"": 118, ""count"": 2}, {""val"": 141, ""count"": 2}, {""val"": 73, ""count"": 2}, {""val"": 138, ""count"": 2}, {""val"": 115, ""count"": 2}, {""val"": 122, ""count"": 2}, {""val"": 111, ""count"": 2}, {""val"": 149, ""count"": 2}, {""val"": 60, ""count"": 1}, {""val"": 146, ""count"": 1}, {""val"": 77, ""count"": 1}, {""val"": 67, ""count"": 1}, {""val"": 93, ""count"": 1}, {""val"": 76, ""count"": 1}, {""val"": 46, ""count"": 1}, {""val"": 53, ""count"": 1}, {""val"": 14, ""count"": 1}, {""val"": 99, ""count"": 1}, {""val"": 177, ""count"": 1}, {""val"": 90, ""count"": 1}, {""val"": 98, ""count"": 1}, {""val"": 52, ""count"": 1}, {""val"": 133, ""count"": 1}, {""val"": 16, ""count"": 1}]","[{""val"": 1000.0, ""count"": 193}, {""val"": 500.0, ""count"": 71}, {""val"": 1024.0, ""count"": 8}, {""val"": 499.7071, ""count"": 6}, {""val"": 1600.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds005059.v1.0.6,0.0
ds005065,[],,Russek2024,21,275,1,Visual,Decision-making,Healthy,68.0,457153490815.0,425.8 GB,openneuro,Heuristics in risky decision-making relate to preferential representation of information MEG data,meg,"[{""val"": 415, ""count"": 210}, {""val"": 341, ""count"": 65}]","[{""val"": 1200.0, ""count"": 272}]",CC0,doi:10.18112/openneuro.ds005065.v1.0.0,1.0
ds005079,[],,Cohen2024,1,60,15,,,,3.8000266666666667,1809231507.0,1.7 GB,openneuro,The Effects of Directed Therapeutic Intent on Live and Damaged Cells,eeg,"[{""val"": 65, ""count"": 60}]","[{""val"": 500.0, ""count"": 60}]",CC0,doi:10.18112/openneuro.ds005079.v2.0.0,1.0
ds005083,[],,Yang2024,61,1357,3,Unknown,Clinical/Intervention,Surgery,,288430.0,281.7 KB,openneuro,Safety and Accuracy of Stereoelectroencephalography for Pediatric Patients with Prior Craniotomy,ieeg,"[{""val"": 105, ""count"": 2}, {""val"": 114, ""count"": 2}, {""val"": 150, ""count"": 1}, {""val"": 102, ""count"": 1}, {""val"": 99, ""count"": 1}, {""val"": 62, ""count"": 1}, {""val"": 98, ""count"": 1}, {""val"": 148, ""count"": 1}, {""val"": 166, ""count"": 1}, {""val"": 138, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 129, ""count"": 1}, {""val"": 61, ""count"": 1}, {""val"": 117, ""count"": 1}, {""val"": 83, ""count"": 1}, {""val"": 230, ""count"": 1}, {""val"": 144, ""count"": 1}, {""val"": 95, ""count"": 1}, {""val"": 100, ""count"": 1}, {""val"": 134, ""count"": 1}, {""val"": 132, ""count"": 1}, {""val"": 112, ""count"": 1}, {""val"": 73, ""count"": 1}, {""val"": 123, ""count"": 1}, {""val"": 93, ""count"": 1}, {""val"": 152, ""count"": 1}, {""val"": 65, ""count"": 1}, {""val"": 103, ""count"": 1}]",[],CC0,doi:10.18112/openneuro.ds005083.v1.0.0,
ds005087,[],,Robinson2024_rapid,20,60,3,Visual,Perception,Healthy,14.447911111111113,13146879384.0,12.2 GB,openneuro,rapid-hemifield-object-eeg,eeg,"[{""val"": 63, ""count"": 60}]","[{""val"": 1000.0, ""count"": 60}]",CC0,doi:10.18112/openneuro.ds005087.v1.0.1,1.0
ds005089,[],,AguadoLopez2024,36,36,1,,,,68.82001666666666,73021312471.0,68.0 GB,openneuro,Proactive selective attention across competition contexts,eeg,"[{""val"": 63, ""count"": 36}]","[{""val"": 1000.0, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds005089.v1.0.1,1.0
ds005095,[],,Zhozhikashvili2024,48,48,1,,,,16.901139444444443,15336170094.0,14.3 GB,openneuro,STERNBERG DIFFICULT,eeg,"[{""val"": 63, ""count"": 48}]","[{""val"": 1000.0, ""count"": 48}]",CC0,doi:10.18112/openneuro.ds005095.v1.0.2,7.0
ds005106,[],,Grootswagers2024,42,42,1,,,,,1331391470.0,1.2 GB,openneuro,200 Objects Infants EEG,eeg,"[{""val"": 33, ""count"": 42}]","[{""val"": 500.0, ""count"": 42}]",CC0,doi:10.18112/openneuro.ds005106.v1.5.0,0.0
ds005107,[],,Xu2024_DEC,21,350,1,Visual,Perception,Healthy,31.625122222222224,29613455000.0,27.6 GB,openneuro,FACE-DEC,meg,"[{""val"": 65, ""count"": 350}]","[{""val"": 1000.0, ""count"": 350}]",CC0,doi:10.18112/openneuro.ds005107.v2.0.0,1.0
ds005114,[],,Cavanagh2024,91,223,1,,,,125.70132055555555,59996318346.0,55.9 GB,openneuro,EEG: DPX Cog Ctl Task in Acute Mild TBI,eeg,"[{""val"": 65, ""count"": 217}, {""val"": 64, ""count"": 6}]","[{""val"": 500.0, ""count"": 223}]",CC0,doi:10.18112/openneuro.ds005114.v1.0.0,0.0
ds005121,[],author_year,Siefert2024,34,39,1,,,,40.52590386284722,9711091695.0,9.0 GB,openneuro,Siefert2024,eeg,"[{""val"": 65, ""count"": 39}]","[{""val"": 512.0, ""count"": 39}]",CC0,doi:10.18112/openneuro.ds005121.v1.0.2,1.0
ds005131,[],,Bialas2024,58,63,2,,,,52.035291666666666,23996523766.0,22.3 GB,openneuro,Evoked responses to elevated sounds,eeg,"[{""val"": 64, ""count"": 63}]","[{""val"": 500.0, ""count"": 63}]",CC0,doi:10.18112/openneuro.ds005131.v1.0.1,0.0
ds005169,[],,Barborica2024,20,112,1,Other,Clinical/Intervention,Epilepsy,0.6533333333333333,4322721427.0,4.0 GB,openneuro,Dataset of intracranial EEG during cortical stimulation evoking visual effects,ieeg,"[{""val"": 82, ""count"": 19}, {""val"": 94, ""count"": 9}, {""val"": 101, ""count"": 6}, {""val"": 136, ""count"": 5}, {""val"": 95, ""count"": 5}, {""val"": 102, ""count"": 5}, {""val"": 193, ""count"": 5}, {""val"": 70, ""count"": 4}, {""val"": 40, ""count"": 4}, {""val"": 83, ""count"": 3}, {""val"": 85, ""count"": 3}, {""val"": 184, ""count"": 3}, {""val"": 79, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 106, ""count"": 2}, {""val"": 143, ""count"": 2}, {""val"": 58, ""count"": 2}, {""val"": 104, ""count"": 2}, {""val"": 91, ""count"": 2}, {""val"": 113, ""count"": 1}, {""val"": 71, ""count"": 1}, {""val"": 88, ""count"": 1}, {""val"": 96, ""count"": 1}, {""val"": 100, ""count"": 1}, {""val"": 80, ""count"": 1}, {""val"": 39, ""count"": 1}, {""val"": 144, ""count"": 1}, {""val"": 105, ""count"": 1}, {""val"": 99, ""count"": 1}, {""val"": 92, ""count"": 1}, {""val"": 160, ""count"": 1}, {""val"": 29, ""count"": 1}, {""val"": 186, ""count"": 1}, {""val"": 69, ""count"": 1}, {""val"": 98, ""count"": 1}, {""val"": 76, ""count"": 1}, {""val"": 109, ""count"": 1}, {""val"": 38, ""count"": 1}, {""val"": 81, ""count"": 1}, {""val"": 114, ""count"": 1}, {""val"": 188, ""count"": 1}, {""val"": 86, ""count"": 1}, {""val"": 89, ""count"": 1}, {""val"": 103, ""count"": 1}]","[{""val"": 4096.0, ""count"": 112}]",CC0,doi:10.18112/openneuro.ds005169.v1.0.0,0.0
ds005170,"[""Chisco""]",canonical,Zhang2024_Chisco,5,225,1,Visual,Motor,Healthy,104.28733333333334,97416063152.0,90.7 GB,openneuro,Chisco,eeg,"[{""val"": 134, ""count"": 1}]",[],CC0,doi:10.18112/openneuro.ds005170.v1.1.2,1.0
ds005178,"[""EESM23""]",canonical,Tabar2024,10,140,1,Sleep,Sleep,Healthy,1012.5174533333333,27636341897.0,25.7 GB,openneuro,Ear-EEG Sleep Monitoring 2023 (EESM23),eeg,"[{""val"": 4, ""count"": 120}, {""val"": 13, ""count"": 20}]","[{""val"": 250.0, ""count"": 140}]",CC0,doi:10.18112/openneuro.ds005178.v1.0.0,
ds005185,"[""EESM19""]",canonical,Mikkelsen2024_Ear_Sleep_Monitoring,20,356,3,Sleep,Sleep,Healthy,1365.566388888889,287335439821.0,267.6 GB,openneuro,Ear-EEG Sleep Monitoring 2019 (EESM19),eeg,"[{""val"": 25, ""count"": 156}]","[{""val"": 500.0, ""count"": 156}]",CC0,doi:10.18112/openneuro.ds005185.v1.0.2,
ds005189,[],,Helbing2024,30,30,1,,,,19.326080555555556,17255876906.0,16.1 GB,openneuro,Search Superiority Recollection Familiarity,eeg,"[{""val"": 62, ""count"": 30}]","[{""val"": 1000.0, ""count"": 30}]",CC0,doi:10.18112/openneuro.ds005189.v1.0.1,0.0
ds005207,"[""Surrey_cEEGrid_sleep""]",canonical,Mikkelsen2024_Surrey_cEEGrid_sleep,20,39,1,,,,422.5796577083333,30627443497.0,28.5 GB,openneuro,Surrey cEEGrid sleep data set,eeg,"[{""val"": 13, ""count"": 8}, {""val"": 24, ""count"": 6}, {""val"": 20, ""count"": 5}, {""val"": 11, ""count"": 5}, {""val"": 27, ""count"": 4}, {""val"": 18, ""count"": 3}, {""val"": 21, ""count"": 3}, {""val"": 15, ""count"": 2}, {""val"": 23, ""count"": 2}, {""val"": 22, ""count"": 1}]","[{""val"": 128.0, ""count"": 20}, {""val"": 250.0, ""count"": 19}]",CC0,doi:10.18112/openneuro.ds005207.v1.0.0,0.0
ds005241,"[""NeuroMorph"", ""neuromorph""]",canonical,Rodriguez2024,24,117,2,Unknown,Other,Healthy,3.7319369444444446,150839266536.0,140.5 GB,openneuro,NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis,meg,"[{""val"": 256, ""count"": 117}]","[{""val"": 1000.0, ""count"": 27}]",CC0,doi:10.18112/openneuro.ds005241.v1.1.0,0.0
ds005261,"[""Todorovic2023""]",author_year,Todorovic2024,17,128,2,Unknown,Learning,Healthy,3.0415982996288142,147368587261.0,137.2 GB,openneuro,Gloups_MEG,meg,"[{""val"": 248, ""count"": 71}, {""val"": 278, ""count"": 31}, {""val"": 245, ""count"": 24}]","[{""val"": 2034.5100996195154, ""count"": 31}, {""val"": 2034.5101318359375, ""count"": 7}]",CC0,doi:10.18112/openneuro.ds005261.v3.0.0,0.0
ds005262,"[""ArEEG""]",canonical,Metwalli2024,12,186,1,,,,25.04766111111111,722210589.0,688.8 MB,openneuro,ArEEG: Arabic Inner Speech EEG dataset,eeg,"[{""val"": 8, ""count"": 186}]","[{""val"": 250.0, ""count"": 186}]",CC0,doi:10.18112/openneuro.ds005262.v1.0.1,0.0
ds005273,[],,Esteban2024,33,33,1,,,,58.05492972222223,47690881750.0,44.4 GB,openneuro,Neural representation of consciously seen and unseen information,eeg,"[{""val"": 63, ""count"": 33}]","[{""val"": 1000.0, ""count"": 33}]",CC0,doi:10.18112/openneuro.ds005273.v1.0.0,0.0
ds005274,[],author_year,Ito2024,22,22,1,Unknown,Unknown,Healthy,1.6619166666666665,75399884.0,71.9 MB,openneuro,UV_EEG,eeg,"[{""val"": 6, ""count"": 22}]","[{""val"": 500.0, ""count"": 22}]",CC0,doi:10.18112/openneuro.ds005274.v1.0.0,0.0
ds005279,[],,Wei2024,30,90,0,Multisensory,Other,Healthy,3.4166666666666665,63222442220.0,58.9 GB,openneuro,Picture-Word Interference Dataset,meg,[],"[{""val"": 1200, ""count"": 30}]",CC0,doi:10.18112/openneuro.ds005279.v1.0.3,0.0
ds005280,[],,Xiangyue2024_223_BP,223,669,1,Tactile,Perception,Healthy,98.77304805555555,45503186303.0,42.4 GB,openneuro,223 By BP,eeg,"[{""val"": 64, ""count"": 669}]","[{""val"": 1000.0, ""count"": 669}]",CC0,doi:10.18112/openneuro.ds005280.v1.0.0,
ds005284,[],,Xiangyue2024_26_Biosemi,26,26,1,Unknown,Unknown,Healthy,2.3897222222222223,1784094196.0,1.7 GB,openneuro,26 By Biosemi,eeg,"[{""val"": 64, ""count"": 26}]","[{""val"": 1024.0, ""count"": 25}, {""val"": 2048.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds005284.v1.0.0,
ds005285,[],,Xiangyue2024_29_ANT,29,116,1,Tactile,Perception,Healthy,26.726972777777778,12705224701.0,11.8 GB,openneuro,29 By ANT,eeg,"[{""val"": 32, ""count"": 116}]","[{""val"": 1000.0, ""count"": 116}]",CC0,doi:10.18112/openneuro.ds005285.v1.0.0,
ds005286,[],,Xiangyue2024_30_ANT,30,30,1,Tactile,Perception,Healthy,21.157749444444445,10042737622.0,9.4 GB,openneuro,30 By ANT,eeg,"[{""val"": 32, ""count"": 30}]","[{""val"": 1000.0, ""count"": 30}]",CC0,doi:10.18112/openneuro.ds005286.v1.0.0,
ds005289,[],,Xiangyue2024_39_BP,39,195,1,Unknown,Unknown,Unknown,16.56392138888889,7636421599.0,7.1 GB,openneuro,39 By BP,eeg,"[{""val"": 64, ""count"": 195}]","[{""val"": 1000.0, ""count"": 195}]",CC0,doi:10.18112/openneuro.ds005289.v1.0.0,
ds005291,[],,Xiangyue2024_65_ANT,65,65,1,Tactile,Perception,Healthy,47.67444555555556,21968960012.0,20.5 GB,openneuro,65 By ANT,eeg,"[{""val"": 32, ""count"": 65}]","[{""val"": 1000.0, ""count"": 65}]",CC0,doi:10.18112/openneuro.ds005291.v1.0.0,
ds005292,[],,Xiangyue2024_142_Biosemi,142,426,1,Tactile,Perception,Healthy,64.33333333333333,54603307745.0,50.9 GB,openneuro,142 by Biosemi,eeg,"[{""val"": 64, ""count"": 426}]","[{""val"": 1024.0, ""count"": 420}, {""val"": 2048.0, ""count"": 6}]",CC0,doi:10.18112/openneuro.ds005292.v1.0.0,
ds005293,[],,Xiangyue2024_95_BP,95,570,1,Tactile,Perception,Healthy,234.03106055555554,106171437197.0,98.9 GB,openneuro,95 By BP,eeg,"[{""val"": 60, ""count"": 570}]","[{""val"": 1000.0, ""count"": 570}]",CC0,doi:10.18112/openneuro.ds005293.v1.0.0,
ds005296,[],,Emmorey2024,62,62,1,,,,37.20480388888889,9154606499.0,8.5 GB,openneuro,"Assessing sensitivity to semantic and syntactic information in deaf readers: An ERP
study",eeg,"[{""val"": 32, ""count"": 62}]","[{""val"": 500.0, ""count"": 62}]",CC0,doi:10.18112/openneuro.ds005296.v1.0.1,0.0
ds005305,[],,Quentin2024,165,165,1,,,,14.136398111979167,6850532958.0,6.4 GB,openneuro,EEG Resting-state Microstates Correlates of Executive Functions,eeg,"[{""val"": 64, ""count"": 165}]","[{""val"": 512.0, ""count"": 164}, {""val"": 2048.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds005305.v1.0.1,0.0
ds005307,"[""Nierula2019""]",author_year,Nierula2024,7,73,1,Tactile,Perception,Healthy,1.5768097222222224,19411390126.0,18.1 GB,openneuro,Laser-evoked potentials in the human spinal cord and cortex,eeg,"[{""val"": 77, ""count"": 50}, {""val"": 109, ""count"": 18}, {""val"": 110, ""count"": 5}]","[{""val"": 10000.0, ""count"": 73}]",CC0,doi:10.18112/openneuro.ds005307.v1.0.1,1.0
ds005340,[],,Polonenko2024_Fundamental,15,15,1,Auditory,Perception,Healthy,35.29713844444445,10172114688.0,9.5 GB,openneuro,Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech,eeg,"[{""val"": 2, ""count"": 15}]","[{""val"": 10000.0, ""count"": 15}]",CC0,doi:10.18112/openneuro.ds005340.v1.0.4,1.0
ds005342,[],,TrianaGuzman2024,32,32,1,,,,33.01657222222222,2181595802.0,2.0 GB,openneuro,EEG data offline and online during motor imagery for standing and sitting,eeg,"[{""val"": 17, ""count"": 32}]","[{""val"": 250.0, ""count"": 32}]",CC0,doi:10.18112/openneuro.ds005342.v1.0.3,1.0
ds005343,[],,Bagdasarov2024,43,43,1,Multisensory,Perception,Development,14.927455833333333,24373540791.0,22.7 GB,openneuro,Gaffrey Lab Infant Microstates and Attention,eeg,"[{""val"": 129, ""count"": 43}]","[{""val"": 1000.0, ""count"": 43}]",CC0,doi:10.18112/openneuro.ds005343.v1.0.0,
ds005345,"[""LPP""]",canonical,Ma2024,26,26,1,Auditory,Attention,Healthy,19.983922222222223,174527101942.0,162.5 GB,openneuro,Le Petit Prince (LPP) Multi-talker: Naturalistic 7T fMRI and EEG Dataset,eeg,"[{""val"": 64, ""count"": 26}]","[{""val"": 500.0, ""count"": 26}]",CC0,doi:10.18112/openneuro.ds005345.v1.0.1,
ds005346,[],,Li2024_Naturalistic_fMRI_viewing,30,90,3,Multisensory,Memory,Healthy,20.35939638888889,41821126308.0,38.9 GB,openneuro,Naturalistic fMRI and MEG recordings during viewing of a reality TV show,meg,"[{""val"": 66, ""count"": 72}, {""val"": 65, ""count"": 18}]","[{""val"": 1000.0, ""count"": 90}]",CC0,doi:10.18112/openneuro.ds005346.v1.0.5,
ds005356,[],canonical,DS5356_MajorDepression,85,116,1,Visual,Learning,Depression,18.24137361111111,173488425070.0,161.6 GB,openneuro,MEG: Major Depression & Probabilistic Learning Task,meg,"[{""val"": 396, ""count"": 113}, {""val"": 450, ""count"": 2}]","[{""val"": 1000.0, ""count"": 55}]",CC0,doi:10.18112/openneuro.ds005356.v1.5.0,
ds005363,"[""ORHA""]",canonical,Haupt2024_Object,43,43,1,Visual,Perception,Healthy,43.08531583333333,19011100939.0,17.7 GB,openneuro,Object recognition in healthy aging (ORHA) - EEG,eeg,"[{""val"": 64, ""count"": 43}]","[{""val"": 1000.0, ""count"": 43}]",CC0,doi:10.18112/openneuro.ds005363.v1.0.0,1.0
ds005383,"[""TMNRED""]",canonical,Bai2024,30,240,1,Visual,Perception,Healthy,8.32688888888889,375577954.0,358.2 MB,openneuro,"TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments",eeg,"[{""val"": 31, ""count"": 240}]","[{""val"": 200.0, ""count"": 240}]",CC0,doi:10.18112/openneuro.ds005383.v1.0.0,0.0
ds005385,[],,Wascher2024,608,3264,2,Resting State,Resting-state,Healthy,169.35916666666665,79529430433.0,74.1 GB,openneuro,Resting-state EEG data before and after cognitive activity across the adult lifespan and a 5-year follow-up,eeg,"[{""val"": 64, ""count"": 3264}]","[{""val"": 1000.0, ""count"": 3264}]",CC0,doi:10.18112/openneuro.ds005385.v1.0.3,1.0
ds005397,[],,Hilton2024,26,26,1,Visual,Affect,Healthy,27.923140555555555,12872404161.0,12.0 GB,openneuro,Affordances of stairs,eeg,"[{""val"": 64, ""count"": 26}]","[{""val"": 500.0, ""count"": 26}]",CC0,doi:10.18112/openneuro.ds005397.v1.0.4,0.0
ds005398,[],,Zhang2024_Open_Pediatric_Wayne,185,185,1,Sleep,Clinical/Intervention,Epilepsy,90.98912097222222,109729579196.0,102.2 GB,openneuro,"Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)",ieeg,"[{""val"": 128, ""count"": 30}, {""val"": 112, ""count"": 20}, {""val"": 108, ""count"": 8}, {""val"": 104, ""count"": 8}, {""val"": 118, ""count"": 6}, {""val"": 102, ""count"": 5}, {""val"": 124, ""count"": 5}, {""val"": 106, ""count"": 5}, {""val"": 132, ""count"": 4}, {""val"": 120, ""count"": 4}, {""val"": 64, ""count"": 4}, {""val"": 138, ""count"": 4}, {""val"": 100, ""count"": 4}, {""val"": 122, ""count"": 3}, {""val"": 114, ""count"": 3}, {""val"": 130, ""count"": 3}, {""val"": 110, ""count"": 3}, {""val"": 116, ""count"": 3}, {""val"": 74, ""count"": 2}, {""val"": 58, ""count"": 2}, {""val"": 98, ""count"": 2}, {""val"": 86, ""count"": 2}, {""val"": 94, ""count"": 2}, {""val"": 73, ""count"": 2}, {""val"": 140, ""count"": 2}, {""val"": 79, ""count"": 2}, {""val"": 96, ""count"": 2}, {""val"": 70, ""count"": 2}, {""val"": 107, ""count"": 2}, {""val"": 77, ""count"": 2}, {""val"": 150, ""count"": 2}, {""val"": 76, ""count"": 2}, {""val"": 126, ""count"": 2}, {""val"": 144, ""count"": 2}, {""val"": 62, ""count"": 1}, {""val"": 44, ""count"": 1}, {""val"": 149, ""count"": 1}, {""val"": 45, ""count"": 1}, {""val"": 40, ""count"": 1}, {""val"": 80, ""count"": 1}, {""val"": 60, ""count"": 1}, {""val"": 136, ""count"": 1}, {""val"": 32, ""count"": 1}, {""val"": 99, ""count"": 1}, {""val"": 63, ""count"": 1}, {""val"": 101, ""count"": 1}, {""val"": 93, ""count"": 1}, {""val"": 33, ""count"": 1}, {""val"": 133, ""count"": 1}, {""val"": 69, ""count"": 1}, {""val"": 127, ""count"": 1}, {""val"": 56, ""count"": 1}, {""val"": 92, ""count"": 1}, {""val"": 81, ""count"": 1}, {""val"": 84, ""count"": 1}, {""val"": 109, ""count"": 1}, {""val"": 34, ""count"": 1}, {""val"": 156, ""count"": 1}, {""val"": 68, ""count"": 1}, {""val"": 67, ""count"": 1}, {""val"": 95, ""count"": 1}, {""val"": 83, ""count"": 1}, {""val"": 72, ""count"": 1}, {""val"": 111, ""count"": 1}, {""val"": 164, ""count"": 1}]","[{""val"": 1000.0, ""count"": 135}, {""val"": 2000.0, ""count"": 49}, {""val"": 200.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds005398.v1.1.1,1.0
ds005403,"[""Veillette2019""]",author_year,Veillette2024,32,32,1,Auditory,Motor,Healthy,13.38265186111111,127189601509.0,118.5 GB,openneuro,Delayed Auditory Feedback EEG/EGG,eeg,"[{""val"": 66, ""count"": 32}]","[{""val"": 10000.0, ""count"": 32}]",CC0,doi:10.18112/openneuro.ds005403.v1.0.1,1.0
ds005406,"[""Formica2025""]",author_year,Formica2024,29,29,1,Visual,Perception,Healthy,15.4524875,14241904586.0,13.3 GB,openneuro,EEG frequency tagging reveals the integration of dissimilar observed actions,eeg,"[{""val"": 64, ""count"": 29}]","[{""val"": 1000.0, ""count"": 29}]",CC0,doi:10.18112/openneuro.ds005406.v1.0.0,0.0
ds005407,[],,Polonenko2024_effect,25,29,1,Auditory,Perception,Healthy,56.87177694444444,40639241863.0,37.8 GB,openneuro,The effect of speech masking on the subcortical response to speech,eeg,"[{""val"": 2, ""count"": 29}]","[{""val"": 10000.0, ""count"": 29}]",CC0,doi:10.18112/openneuro.ds005407.v1.0.1,
ds005408,[],,Polonenko2024_effect_speech,25,29,1,Auditory,Perception,Healthy,56.87177694444444,16418282216.0,15.3 GB,openneuro,The effect of speech masking on the subcortical response to speech,eeg,"[{""val"": 2, ""count"": 29}]","[{""val"": 10000.0, ""count"": 29}]",CC0,doi:10.18112/openneuro.ds005408.v1.0.0,
ds005410,[],,Pavlov2024_Semantic_conditioning,81,81,1,Visual,Affect,Healthy,22.97631888888889,21213480734.0,19.8 GB,openneuro,Semantic_conditioning,eeg,"[{""val"": 63, ""count"": 81}]","[{""val"": 1000.0, ""count"": 81}]",CC0,doi:10.18112/openneuro.ds005410.v1.0.1,1.0
ds005411,[],,Herrema2024_Free,47,193,1,Visual,Memory,Epilepsy,140.0236284722222,168961242901.0,157.4 GB,openneuro,Free Recall of Word Lists with Repeated Items,ieeg,"[{""val"": 120, ""count"": 12}, {""val"": 118, ""count"": 10}, {""val"": 109, ""count"": 9}, {""val"": 153, ""count"": 7}, {""val"": 168, ""count"": 7}, {""val"": 122, ""count"": 6}, {""val"": 116, ""count"": 6}, {""val"": 126, ""count"": 6}, {""val"": 110, ""count"": 6}, {""val"": 182, ""count"": 5}, {""val"": 106, ""count"": 5}, {""val"": 192, ""count"": 4}, {""val"": 115, ""count"": 4}, {""val"": 169, ""count"": 4}, {""val"": 211, ""count"": 4}, {""val"": 108, ""count"": 4}, {""val"": 200, ""count"": 4}, {""val"": 167, ""count"": 4}, {""val"": 134, ""count"": 4}, {""val"": 155, ""count"": 4}, {""val"": 152, ""count"": 4}, {""val"": 133, ""count"": 3}, {""val"": 132, ""count"": 3}, {""val"": 105, ""count"": 3}, {""val"": 127, ""count"": 3}, {""val"": 141, ""count"": 3}, {""val"": 121, ""count"": 3}, {""val"": 184, ""count"": 2}, {""val"": 166, ""count"": 2}, {""val"": 44, ""count"": 2}, {""val"": 55, ""count"": 2}, {""val"": 186, ""count"": 2}, {""val"": 213, ""count"": 2}, {""val"": 187, ""count"": 2}, {""val"": 99, ""count"": 2}, {""val"": 173, ""count"": 2}, {""val"": 181, ""count"": 2}, {""val"": 96, ""count"": 2}, {""val"": 160, ""count"": 2}, {""val"": 94, ""count"": 2}, {""val"": 195, ""count"": 2}, {""val"": 140, ""count"": 2}, {""val"": 84, ""count"": 2}, {""val"": 154, ""count"": 1}, {""val"": 98, ""count"": 1}, {""val"": 230, ""count"": 1}, {""val"": 107, ""count"": 1}, {""val"": 159, ""count"": 1}, {""val"": 111, ""count"": 1}, {""val"": 138, ""count"": 1}, {""val"": 236, ""count"": 1}, {""val"": 123, ""count"": 1}, {""val"": 158, ""count"": 1}, {""val"": 210, ""count"": 1}, {""val"": 202, ""count"": 1}, {""val"": 128, ""count"": 1}, {""val"": 101, ""count"": 1}, {""val"": 232, ""count"": 1}, {""val"": 176, ""count"": 1}, {""val"": 165, ""count"": 1}, {""val"": 218, ""count"": 1}, {""val"": 239, ""count"": 1}, {""val"": 104, ""count"": 1}, {""val"": 142, ""count"": 1}, {""val"": 119, ""count"": 1}, {""val"": 215, ""count"": 1}, {""val"": 129, ""count"": 1}]","[{""val"": 1000.0, ""count"": 150}, {""val"": 2048.0, ""count"": 21}, {""val"": 2000.0, ""count"": 10}, {""val"": 512.0, ""count"": 8}, {""val"": 1024.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds005411.v1.0.0,0.0
ds005415,[],,Rockhill2024,13,13,1,Multisensory,Perception,Epilepsy,4.21944125,8015284412.0,7.5 GB,openneuro,Numbers,ieeg,"[{""val"": 182, ""count"": 2}, {""val"": 228, ""count"": 1}, {""val"": 230, ""count"": 1}, {""val"": 210, ""count"": 1}, {""val"": 194, ""count"": 1}, {""val"": 246, ""count"": 1}, {""val"": 188, ""count"": 1}, {""val"": 266, ""count"": 1}, {""val"": 192, ""count"": 1}, {""val"": 224, ""count"": 1}, {""val"": 202, ""count"": 1}, {""val"": 200, ""count"": 1}]","[{""val"": 1000.0, ""count"": 10}, {""val"": 2000.0, ""count"": 3}]",CC0,doi:10.18112/openneuro.ds005415.v1.0.0,1.0
ds005416,[],,Wu2024,23,23,1,Visual,Resting-state,Healthy,24.67972222222222,22869324774.0,21.3 GB,openneuro,Fatigue Characterization of EEG under Mixed Reality Stereo Vision,eeg,"[{""val"": 64, ""count"": 23}]","[{""val"": 1000.0, ""count"": 23}]",CC0,doi:10.18112/openneuro.ds005416.v1.0.1,0.0
ds005420,"[""Gama2019""]",author_year,Gama2024,37,72,2,Resting State,Resting-state,Healthy,5.411903888888888,390188994.0,372.1 MB,openneuro,Resting state EEG with closed eyes and open eyes in females from 60 to 80 years old,eeg,"[{""val"": 20, ""count"": 72}]","[{""val"": 500.0, ""count"": 72}]",CC0,doi:10.18112/openneuro.ds005420.v1.0.0,0.0
ds005429,[],,Rutiku2024,15,61,3,Auditory,Attention,Healthy,14.390138722222222,17684554701.0,16.5 GB,openneuro,"Auditory oddball comparison (Optimum-1, Learning-oddball, and the local–global paradigm)",eeg,"[{""val"": 64, ""count"": 61}]","[{""val"": 2500.0, ""count"": 57}, {""val"": 5000.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds005429.v1.0.0,0.0
ds005448,"[""STReEF""]",canonical,Jelsma2024,13,18,1,Other,Clinical/Intervention,Epilepsy,12.382256944444444,48025611704.0,44.7 GB,openneuro,STReEF,ieeg,"[{""val"": 133, ""count"": 14}, {""val"": 109, ""count"": 2}, {""val"": 95, ""count"": 1}, {""val"": 161, ""count"": 1}]","[{""val"": 2048.0, ""count"": 18}]",CC0,doi:10.18112/openneuro.ds005448.v1.0.0,
ds005473,"[""Zhao2024""]",author_year,Xiangyue2024_29_BP,29,58,1,Unknown,Unknown,Healthy,14.631388888888889,6637997774.0,6.2 GB,openneuro,29 By BP,eeg,"[{""val"": 64, ""count"": 58}]","[{""val"": 1000.0, ""count"": 58}]",CC0,doi:10.18112/openneuro.ds005473.v1.0.0,
ds005486,[],author_year,Chowdhury2024,159,445,1,Resting State,Resting-state,Unknown,56.76204444444444,398401151423.0,371.0 GB,openneuro,PREDICT,eeg,"[{""val"": 66, ""count"": 445}]","[{""val"": 5000.0, ""count"": 399}, {""val"": 25000.0, ""count"": 46}]",CC0,doi:10.18112/openneuro.ds005486.v1.0.1,0.0
ds005489,[],,Herrema2024_Free_Recall,37,154,1,Visual,Memory,Unknown,138.17323425954314,69728763972.0,64.9 GB,openneuro,Free Recall with Open-Loop Stimulation at Encoding,ieeg,"[{""val"": 100, ""count"": 12}, {""val"": 64, ""count"": 10}, {""val"": 141, ""count"": 9}, {""val"": 118, ""count"": 8}, {""val"": 96, ""count"": 8}, {""val"": 136, ""count"": 7}, {""val"": 109, ""count"": 7}, {""val"": 88, ""count"": 6}, {""val"": 68, ""count"": 6}, {""val"": 72, ""count"": 6}, {""val"": 87, ""count"": 4}, {""val"": 126, ""count"": 4}, {""val"": 75, ""count"": 4}, {""val"": 56, ""count"": 4}, {""val"": 110, ""count"": 4}, {""val"": 70, ""count"": 4}, {""val"": 93, ""count"": 3}, {""val"": 156, ""count"": 3}, {""val"": 76, ""count"": 3}, {""val"": 85, ""count"": 3}, {""val"": 74, ""count"": 3}, {""val"": 58, ""count"": 3}, {""val"": 120, ""count"": 3}, {""val"": 104, ""count"": 2}, {""val"": 124, ""count"": 2}, {""val"": 112, ""count"": 2}, {""val"": 134, ""count"": 2}, {""val"": 128, ""count"": 2}, {""val"": 80, ""count"": 2}, {""val"": 138, ""count"": 2}, {""val"": 99, ""count"": 2}, {""val"": 123, ""count"": 2}, {""val"": 108, ""count"": 2}, {""val"": 18, ""count"": 1}, {""val"": 163, ""count"": 1}, {""val"": 14, ""count"": 1}, {""val"": 20, ""count"": 1}, {""val"": 83, ""count"": 1}, {""val"": 101, ""count"": 1}, {""val"": 97, ""count"": 1}, {""val"": 114, ""count"": 1}, {""val"": 177, ""count"": 1}, {""val"": 16, ""count"": 1}]","[{""val"": 500.0, ""count"": 90}, {""val"": 1000.0, ""count"": 46}, {""val"": 1600.0, ""count"": 10}, {""val"": 513.0, ""count"": 4}, {""val"": 256.0, ""count"": 2}, {""val"": 499.7071, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds005489.v1.0.3,0.0
ds005491,"[""catFR_open_loop"", ""RAM_catFR"", ""catFR_stim""]",canonical,Herrema2024_Categorized,19,51,1,Visual,Clinical/Intervention,Unknown,46.67650149705261,24202629465.0,22.5 GB,openneuro,Categorized Free Recall with Open-Loop Stimulation at Encoding,ieeg,"[{""val"": 64, ""count"": 5}, {""val"": 126, ""count"": 5}, {""val"": 88, ""count"": 4}, {""val"": 85, ""count"": 3}, {""val"": 93, ""count"": 3}, {""val"": 113, ""count"": 2}, {""val"": 163, ""count"": 2}, {""val"": 104, ""count"": 2}, {""val"": 155, ""count"": 2}, {""val"": 133, ""count"": 2}, {""val"": 116, ""count"": 2}, {""val"": 119, ""count"": 1}, {""val"": 72, ""count"": 1}, {""val"": 68, ""count"": 1}, {""val"": 92, ""count"": 1}, {""val"": 96, ""count"": 1}, {""val"": 127, ""count"": 1}, {""val"": 177, ""count"": 1}, {""val"": 146, ""count"": 1}, {""val"": 78, ""count"": 1}, {""val"": 128, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 110, ""count"": 1}, {""val"": 70, ""count"": 1}, {""val"": 80, ""count"": 1}, {""val"": 14, ""count"": 1}, {""val"": 115, ""count"": 1}, {""val"": 130, ""count"": 1}, {""val"": 112, ""count"": 1}, {""val"": 16, ""count"": 1}]","[{""val"": 500.0, ""count"": 39}, {""val"": 1600.0, ""count"": 6}, {""val"": 999.0, ""count"": 4}, {""val"": 1000.0, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds005491.v1.0.0,0.0
ds005494,"[""Herrema2024""]",author_year,Herrema2024_Cued,20,51,1,Visual,Clinical/Intervention,Unknown,55.08165333333333,28279607081.0,26.3 GB,openneuro,Cued Recall of Paired Associates with Open-Loop Stimulation at Encoding or Retrieval,ieeg,"[{""val"": 100, ""count"": 4}, {""val"": 88, ""count"": 4}, {""val"": 68, ""count"": 3}, {""val"": 128, ""count"": 3}, {""val"": 177, ""count"": 3}, {""val"": 72, ""count"": 3}, {""val"": 141, ""count"": 2}, {""val"": 112, ""count"": 2}, {""val"": 64, ""count"": 2}, {""val"": 114, ""count"": 2}, {""val"": 14, ""count"": 2}, {""val"": 85, ""count"": 2}, {""val"": 16, ""count"": 2}, {""val"": 84, ""count"": 1}, {""val"": 111, ""count"": 1}, {""val"": 93, ""count"": 1}, {""val"": 122, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 95, ""count"": 1}, {""val"": 107, ""count"": 1}, {""val"": 102, ""count"": 1}, {""val"": 86, ""count"": 1}, {""val"": 110, ""count"": 1}, {""val"": 96, ""count"": 1}, {""val"": 146, ""count"": 1}, {""val"": 104, ""count"": 1}, {""val"": 119, ""count"": 1}, {""val"": 121, ""count"": 1}, {""val"": 138, ""count"": 1}, {""val"": 106, ""count"": 1}]","[{""val"": 500.0, ""count"": 35}, {""val"": 1000.0, ""count"": 16}]",CC0,doi:10.18112/openneuro.ds005494.v1.0.1,0.0
ds005505,"[""HBN_r1""]",canonical,Shirazi2024_R1,136,1342,10,Visual,Clinical/Intervention,Development,117.5389038888889,110708612638.0,103.1 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 1,eeg,"[{""val"": 129, ""count"": 1342}]","[{""val"": 500.0, ""count"": 1342}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005505.v1.0.1,1.0
ds005506,"[""HBN_r2""]",canonical,Shirazi2024_R2,150,1405,10,Visual,Clinical/Intervention,Development,127.52424388888889,120126392039.0,111.9 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 2,eeg,"[{""val"": 129, ""count"": 1405}]","[{""val"": 500.0, ""count"": 1405}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005506.v1.0.1,1.0
ds005507,"[""HBN_r3""]",canonical,Shirazi2024_R3,184,1812,10,Visual,Clinical/Intervention,Development,158.8272261111111,149646643076.0,139.4 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 3,eeg,"[{""val"": 129, ""count"": 1812}]","[{""val"": 500.0, ""count"": 1812}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005507.v1.0.1,1.0
ds005508,"[""HBN_r4""]",canonical,Shirazi2024_R4,324,3342,10,Visual,Clinical/Intervention,Development,261.8067727777778,246753616656.0,229.8 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 4,eeg,"[{""val"": 129, ""count"": 3342}]","[{""val"": 500.0, ""count"": 3342}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005508.v1.0.1,1.0
ds005509,"[""HBN_r5""]",canonical,Shirazi2024_R5,330,3326,10,Visual,Clinical/Intervention,Development,255.34662,240701002046.0,224.2 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 5,eeg,"[{""val"": 129, ""count"": 3326}]","[{""val"": 500.0, ""count"": 3326}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005509.v1.0.1,1.0
ds005510,"[""HBN_r6""]",canonical,Shirazi2024_R6,135,1227,10,Visual,Clinical/Intervention,Development,103.45367166666666,97492911148.0,90.8 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 6,eeg,"[{""val"": 129, ""count"": 1227}]","[{""val"": 500.0, ""count"": 1227}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005510.v1.0.1,1.0
ds005512,"[""HBN_r8""]",canonical,Shirazi2024_R8,257,2320,10,Multisensory,Clinical/Intervention,Development,179.12267277777778,168778412421.0,157.2 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 8,eeg,"[{""val"": 129, ""count"": 2320}]","[{""val"": 500.0, ""count"": 2320}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005512.v1.0.1,2.0
ds005514,"[""HBN_r9""]",canonical,Shirazi2024_R9,295,2885,10,Visual,Clinical/Intervention,Development,210.79156111111112,198677619354.0,185.0 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 9,eeg,"[{""val"": 129, ""count"": 2885}]","[{""val"": 500.0, ""count"": 2885}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005514.v1.0.1,1.0
ds005515,"[""HBN_r10""]",canonical,Shirazi2024_R10,533,2516,8,Visual,Clinical/Intervention,Development,183.12360166666667,172385562881.0,160.5 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 10,eeg,"[{""val"": 129, ""count"": 2516}]","[{""val"": 500.0, ""count"": 2516}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005515.v1.0.1,
ds005516,"[""HBN_r11""]",canonical,Shirazi2024_R11,430,3397,8,Visual,Clinical/Intervention,Development,250.45184833333332,235310606952.0,219.2 GB,openneuro,Healthy Brain Network (HBN) EEG - Release 11,eeg,"[{""val"": 129, ""count"": 3397}]","[{""val"": 500.0, ""count"": 3397}]",CC-BY-SA 4.0,doi:10.18112/openneuro.ds005516.v1.0.1,
ds005520,[],,Li2024_Research_supporting_playing,23,69,3,Visual,Other,Healthy,48.870593611111104,47150724861.0,43.9 GB,openneuro,Research data supporting 'EEG recording during playing MOBA game',eeg,"[{""val"": 67, ""count"": 69}]","[{""val"": 1000.0, ""count"": 69}]",CC0,doi:10.18112/openneuro.ds005520.v1.0.1,0.0
ds005522,[],,Herrema2024_Spatial,55,176,1,Visual,Memory,Unknown,145.237675247194,115392336164.0,107.5 GB,openneuro,Spatial Navigation Memory of Object Locations,ieeg,"[{""val"": 133, ""count"": 8}, {""val"": 110, ""count"": 8}, {""val"": 88, ""count"": 7}, {""val"": 120, ""count"": 7}, {""val"": 72, ""count"": 6}, {""val"": 188, ""count"": 6}, {""val"": 173, ""count"": 6}, {""val"": 126, ""count"": 6}, {""val"": 108, ""count"": 5}, {""val"": 56, ""count"": 5}, {""val"": 46, ""count"": 4}, {""val"": 128, ""count"": 4}, {""val"": 127, ""count"": 4}, {""val"": 68, ""count"": 4}, {""val"": 112, ""count"": 4}, {""val"": 64, ""count"": 4}, {""val"": 144, ""count"": 3}, {""val"": 146, ""count"": 3}, {""val"": 92, ""count"": 3}, {""val"": 123, ""count"": 3}, {""val"": 186, ""count"": 3}, {""val"": 124, ""count"": 3}, {""val"": 50, ""count"": 3}, {""val"": 104, ""count"": 3}, {""val"": 182, ""count"": 3}, {""val"": 86, ""count"": 3}, {""val"": 160, ""count"": 2}, {""val"": 59, ""count"": 2}, {""val"": 180, ""count"": 2}, {""val"": 138, ""count"": 2}, {""val"": 163, ""count"": 2}, {""val"": 85, ""count"": 2}, {""val"": 75, ""count"": 2}, {""val"": 140, ""count"": 2}, {""val"": 111, ""count"": 2}, {""val"": 70, ""count"": 2}, {""val"": 130, ""count"": 2}, {""val"": 63, ""count"": 2}, {""val"": 170, ""count"": 2}, {""val"": 96, ""count"": 2}, {""val"": 166, ""count"": 2}, {""val"": 158, ""count"": 2}, {""val"": 118, ""count"": 2}, {""val"": 100, ""count"": 2}, {""val"": 90, ""count"": 1}, {""val"": 54, ""count"": 1}, {""val"": 151, ""count"": 1}, {""val"": 105, ""count"": 1}, {""val"": 109, ""count"": 1}, {""val"": 94, ""count"": 1}, {""val"": 149, ""count"": 1}, {""val"": 172, ""count"": 1}, {""val"": 122, ""count"": 1}, {""val"": 174, ""count"": 1}, {""val"": 76, ""count"": 1}, {""val"": 78, ""count"": 1}, {""val"": 178, ""count"": 1}, {""val"": 84, ""count"": 1}, {""val"": 165, ""count"": 1}, {""val"": 125, ""count"": 1}, {""val"": 177, ""count"": 1}, {""val"": 169, ""count"": 1}, {""val"": 136, ""count"": 1}, {""val"": 80, ""count"": 1}, {""val"": 60, ""count"": 1}, {""val"": 116, ""count"": 1}]","[{""val"": 1000.0, ""count"": 70}, {""val"": 500.0, ""count"": 61}, {""val"": 1600.0, ""count"": 26}, {""val"": 999.0, ""count"": 13}, {""val"": 2000.0, ""count"": 4}, {""val"": 1999.0, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds005522.v1.0.0,0.0
ds005523,[],,Herrema2024_Spatial_Memory,21,102,1,Visual,Memory,Surgery,84.3298929568347,74834541025.0,69.7 GB,openneuro,Spatial Memory of Object Locations with Open-Loop Stimulation at Encoding,ieeg,"[{""val"": 166, ""count"": 7}, {""val"": 144, ""count"": 7}, {""val"": 180, ""count"": 7}, {""val"": 182, ""count"": 7}, {""val"": 126, ""count"": 6}, {""val"": 156, ""count"": 5}, {""val"": 56, ""count"": 5}, {""val"": 50, ""count"": 5}, {""val"": 118, ""count"": 4}, {""val"": 109, ""count"": 4}, {""val"": 141, ""count"": 4}, {""val"": 68, ""count"": 3}, {""val"": 64, ""count"": 3}, {""val"": 133, ""count"": 3}, {""val"": 138, ""count"": 2}, {""val"": 87, ""count"": 2}, {""val"": 104, ""count"": 2}, {""val"": 120, ""count"": 2}, {""val"": 108, ""count"": 2}, {""val"": 123, ""count"": 2}, {""val"": 76, ""count"": 2}, {""val"": 85, ""count"": 2}, {""val"": 174, ""count"": 2}, {""val"": 94, ""count"": 2}, {""val"": 100, ""count"": 2}, {""val"": 163, ""count"": 1}, {""val"": 110, ""count"": 1}, {""val"": 173, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 188, ""count"": 1}, {""val"": 88, ""count"": 1}, {""val"": 143, ""count"": 1}, {""val"": 92, ""count"": 1}, {""val"": 80, ""count"": 1}, {""val"": 112, ""count"": 1}]","[{""val"": 1000.0, ""count"": 58}, {""val"": 1600.0, ""count"": 22}, {""val"": 500.0, ""count"": 18}, {""val"": 999.0, ""count"": 2}, {""val"": 499.7071, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds005523.v1.0.1,0.0
ds005530,[],,Greco2024,17,21,1,Multisensory,Sleep,Healthy,144.7749161111111,6949640968.0,6.5 GB,openneuro,Depotentiation of emotional reactivity using TMR during REM sleep,eeg,"[{""val"": 10, ""count"": 21}]","[{""val"": 500.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds005530.v1.0.9,0.0
ds005540,[],,Xin2024,59,103,1,Visual,Affect,Healthy,167.10194444444446,50796471315.0,47.3 GB,openneuro,EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding,eeg,"[{""val"": 68, ""count"": 103}]","[{""val"": 600.0, ""count"": 95}, {""val"": 1200.0, ""count"": 8}]",CC0,doi:10.18112/openneuro.ds005540.v1.0.7,0.0
ds005545,"[""Kanno2025""]",author_year,Kanno2024,106,336,1,Auditory,Other,Surgery,117.62165555555556,42905692557.0,40.0 GB,openneuro,Auditory naming,ieeg,"[{""val"": 128, ""count"": 237}, {""val"": 138, ""count"": 14}, {""val"": 136, ""count"": 11}, {""val"": 134, ""count"": 11}, {""val"": 140, ""count"": 8}, {""val"": 112, ""count"": 6}, {""val"": 110, ""count"": 6}, {""val"": 156, ""count"": 5}, {""val"": 142, ""count"": 5}, {""val"": 150, ""count"": 5}, {""val"": 164, ""count"": 4}, {""val"": 132, ""count"": 4}, {""val"": 144, ""count"": 4}, {""val"": 148, ""count"": 4}, {""val"": 118, ""count"": 3}, {""val"": 116, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 96, ""count"": 3}]","[{""val"": 1000.0, ""count"": 336}]",CC0,doi:10.18112/openneuro.ds005545.v1.0.3,0.0
ds005555,"[""BOAS""]",canonical,LopezLarraz2024,128,256,1,Sleep,Sleep,Healthy,2002.5916666666667,35921406226.0,33.5 GB,openneuro,The Bitbrain Open Access Sleep (BOAS) dataset,eeg,"[{""val"": 9, ""count"": 96}, {""val"": 11, ""count"": 68}, {""val"": 14, ""count"": 25}, {""val"": 16, ""count"": 19}, {""val"": 3, ""count"": 18}, {""val"": 2, ""count"": 16}, {""val"": 8, ""count"": 9}, {""val"": 15, ""count"": 5}]","[{""val"": 256.0, ""count"": 256}]",CC0,doi:10.18112/openneuro.ds005555.v1.1.1,0.0
ds005557,[],,Herrema2024_Classifier,16,58,1,Visual,Memory,Other,51.07257555555555,37243089880.0,34.7 GB,openneuro,Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier),ieeg,"[{""val"": 110, ""count"": 7}, {""val"": 112, ""count"": 6}, {""val"": 76, ""count"": 5}, {""val"": 109, ""count"": 4}, {""val"": 126, ""count"": 4}, {""val"": 108, ""count"": 4}, {""val"": 127, ""count"": 3}, {""val"": 67, ""count"": 2}, {""val"": 70, ""count"": 2}, {""val"": 62, ""count"": 2}, {""val"": 64, ""count"": 2}, {""val"": 60, ""count"": 2}, {""val"": 97, ""count"": 2}, {""val"": 120, ""count"": 2}, {""val"": 83, ""count"": 2}, {""val"": 128, ""count"": 2}, {""val"": 125, ""count"": 1}, {""val"": 99, ""count"": 1}, {""val"": 95, ""count"": 1}, {""val"": 118, ""count"": 1}, {""val"": 121, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 136, ""count"": 1}]","[{""val"": 1000.0, ""count"": 58}]",CC0,doi:10.18112/openneuro.ds005557.v1.0.0,0.0
ds005558,"[""catFR_closed_loop""]",canonical,Herrema2024_Categorized_Free,7,22,1,Visual,Memory,Surgery,17.006120555555555,13091493612.0,12.2 GB,openneuro,Categorized Free Recall with Closed-Loop Stimulation at Encoding (Encoding Classifier),ieeg,"[{""val"": 126, ""count"": 3}, {""val"": 124, ""count"": 2}, {""val"": 128, ""count"": 2}, {""val"": 113, ""count"": 2}, {""val"": 114, ""count"": 2}, {""val"": 78, ""count"": 2}, {""val"": 68, ""count"": 2}, {""val"": 156, ""count"": 2}, {""val"": 122, ""count"": 1}, {""val"": 90, ""count"": 1}, {""val"": 56, ""count"": 1}, {""val"": 110, ""count"": 1}, {""val"": 64, ""count"": 1}]","[{""val"": 1000.0, ""count"": 22}]",CC0,doi:10.18112/openneuro.ds005558.v1.0.0,0.0
ds005565,[],,Lee2024_StudyWITH,24,24,1,Visual,Memory,Healthy,11.435694444444444,2816585202.0,2.6 GB,openneuro,"Neural associations between fingerspelling, print, and signs: An ERP priming study with deaf readers",eeg,"[{""val"": 32, ""count"": 24}]","[{""val"": 500.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds005565.v1.0.3,1.0
ds005571,[],author_year,MartinezMolina2024,24,45,2,Unknown,Attention,Healthy,28.21085,67959727373.0,63.3 GB,openneuro,Expectation of Conflict Stimuli,eeg,"[{""val"": 66, ""count"": 41}, {""val"": 67, ""count"": 4}]","[{""val"": 5000.0, ""count"": 45}]",CC0,doi:10.18112/openneuro.ds005571.v1.0.1,1.0
ds005574,"[""Podcast""]",canonical,Zada2024,9,9,1,Auditory,Other,Unknown,4.499995524088542,3483284760.0,3.2 GB,openneuro,"The ""Podcast"" ECoG dataset",ieeg,"[{""val"": 178, ""count"": 1}, {""val"": 174, ""count"": 1}, {""val"": 114, ""count"": 1}, {""val"": 138, ""count"": 1}, {""val"": 264, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 167, ""count"": 1}, {""val"": 91, ""count"": 1}, {""val"": 205, ""count"": 1}]","[{""val"": 512.0, ""count"": 8}, {""val"": 2048.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds005574.v1.0.2,
ds005586,[],,Baykan2024,23,23,1,Visual,Perception,Healthy,33.52867694444444,30422094662.0,28.3 GB,openneuro,Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes,eeg,"[{""val"": 63, ""count"": 23}]","[{""val"": 1000.0, ""count"": 23}]",CC0,doi:10.18112/openneuro.ds005586.v2.0.0,1.0
ds005594,[],author_year,Taylor2024,16,16,1,Visual,Perception,Healthy,12.933862222222222,11695588974.0,10.9 GB,openneuro,Alphabetic Decision Task (Arial Light Font),eeg,"[{""val"": 66, ""count"": 16}]","[{""val"": 1000.0, ""count"": 16}]",CC0,doi:10.18112/openneuro.ds005594.v1.0.3,1.0
ds005620,[],,Bajwa2024,21,202,3,Anesthesia,Clinical/Intervention,Healthy,17.939717444444444,83002662733.0,77.3 GB,openneuro,A repeated awakening study exploring the capacity of complexity measures to capture dreaming during propofol sedation,eeg,"[{""val"": 64, ""count"": 132}, {""val"": 65, ""count"": 70}]","[{""val"": 5000.0, ""count"": 202}]",CC0,doi:10.18112/openneuro.ds005620.v1.0.0,0.0
ds005624,[],author_year,DS5624_ColorChangeDetection,24,35,1,Visual,Memory,Unknown,11.400254177517361,14768426442.0,13.8 GB,openneuro,Color Change Detection Task,ieeg,"[{""val"": 74, ""count"": 4}, {""val"": 223, ""count"": 3}, {""val"": 172, ""count"": 3}, {""val"": 95, ""count"": 2}, {""val"": 162, ""count"": 2}, {""val"": 111, ""count"": 2}, {""val"": 152, ""count"": 2}, {""val"": 115, ""count"": 2}, {""val"": 151, ""count"": 2}, {""val"": 100, ""count"": 2}, {""val"": 205, ""count"": 1}, {""val"": 189, ""count"": 1}, {""val"": 191, ""count"": 1}, {""val"": 228, ""count"": 1}, {""val"": 173, ""count"": 1}, {""val"": 101, ""count"": 1}, {""val"": 118, ""count"": 1}, {""val"": 127, ""count"": 1}, {""val"": 128, ""count"": 1}, {""val"": 123, ""count"": 1}, {""val"": 166, ""count"": 1}]","[{""val"": 512.0, ""count"": 26}, {""val"": 1024.0, ""count"": 9}]",CC0,doi:10.18112/openneuro.ds005624.v1.0.0,0.0
ds005628,[],,RosadoAiza2024,102,306,1,Multisensory,Attention,Healthy,20.953197777777778,664444547.0,633.7 MB,openneuro,Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site,eeg,"[{""val"": 8, ""count"": 306}]","[{""val"": 250.0, ""count"": 306}]",CC0,doi:10.18112/openneuro.ds005628.v1.0.0,
ds005642,[],,Robinson2024_illusory,21,21,1,Visual,Perception,Healthy,18.59861111111111,14838061051.0,13.8 GB,openneuro,illusory-face-eeg,eeg,"[{""val"": 68, ""count"": 21}]","[{""val"": 1024.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds005642.v1.0.1,
ds005648,[],,Kidder2024,21,21,1,Visual,Perception,Healthy,11.576944444444445,16672927073.0,15.5 GB,openneuro,Mapping object space dimensions: new insights from temporal dynamics,eeg,"[{""val"": 64, ""count"": 21}]","[{""val"": 2048.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds005648.v1.0.3,
ds005662,[],,Smit2024,80,80,1,Visual,Perception,Healthy,80.46722222222222,115754787329.0,107.8 GB,openneuro,"A comprehensive EEG dataset for investigating
visual touch perception",eeg,"[{""val"": 65, ""count"": 80}]","[{""val"": 2048.0, ""count"": 80}]",CC0,doi:10.18112/openneuro.ds005662.v2.0.1,
ds005670,[],,Xu2024_SEEG_Resting_State,2,2,1,Resting State,Resting-state,Epilepsy,0.24251,743242149.0,708.8 MB,openneuro,SEEG Resting State Recording,ieeg,"[{""val"": 186, ""count"": 1}, {""val"": 238, ""count"": 1}]","[{""val"": 2000.0, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds005670.v1.0.0,0.0
ds005672,[],,Zhiyuan2024,3,3,1,Visual,Memory,Healthy,4.5855,4545640816.0,4.2 GB,openneuro,PerceiveImagine,eeg,"[{""val"": 69, ""count"": 2}, {""val"": 65, ""count"": 1}]","[{""val"": 1000.0, ""count"": 3}]",CC0,doi:10.18112/openneuro.ds005672.v1.0.0,2.0
ds005688,[],,Tan2024,20,89,5,Visual,Clinical/Intervention,Healthy,1.7316666666666667,9036020603.0,8.4 GB,openneuro,visStim,eeg,"[{""val"": 5, ""count"": 86}, {""val"": 1, ""count"": 3}]","[{""val"": 10000.0, ""count"": 74}, {""val"": 20000.0, ""count"": 15}]",CC0,doi:10.18112/openneuro.ds005688.v1.0.1,0.0
ds005691,[],,Stenner2024_SpinalExpect,8,8,1,Multisensory,Attention,Other,5.919116666666667,758159062.0,723.0 MB,openneuro,SpinalExpect_Invasive,ieeg,"[{""val"": 7, ""count"": 7}, {""val"": 8, ""count"": 1}]","[{""val"": 2500.0, ""count"": 8}]",CC0,doi:10.18112/openneuro.ds005691.v1.0.0,0.0
ds005692,[],,Stenner2024_SpinalExpect_NonInvasive,30,59,1,Multisensory,Attention,Healthy,112.20623327777777,99649236711.0,92.8 GB,openneuro,SpinalExpect_NonInvasive,eeg,"[{""val"": 25, ""count"": 59}]","[{""val"": 5000.0, ""count"": 59}]",CC0,doi:10.18112/openneuro.ds005692.v1.0.0,0.0
ds005697,"[""PerceiveImagine""]",canonical,Li2024_PerceiveImagine,51,51,1,Visual,Memory,Healthy,77.68925,71461601517.0,66.6 GB,openneuro,PerceiveImagine,eeg,"[{""val"": 65, ""count"": 45}, {""val"": 69, ""count"": 6}]","[{""val"": 1000.0, ""count"": 50}]",CC0,doi:10.18112/openneuro.ds005697.v1.0.2,3.0
ds005752,[],canonical,Nugent2024,123,1055,10,Multisensory,Other,Healthy,102.62917361111111,711534899143.0,662.7 GB,openneuro,The NIMH Healthy Research Volunteer Dataset,meg,"[{""val"": 305, ""count"": 240}, {""val"": 306, ""count"": 183}, {""val"": 304, ""count"": 123}, {""val"": 302, ""count"": 117}, {""val"": 303, ""count"": 110}, {""val"": 301, ""count"": 71}, {""val"": 382, ""count"": 59}, {""val"": 300, ""count"": 57}, {""val"": 378, ""count"": 20}, {""val"": 377, ""count"": 16}, {""val"": 379, ""count"": 16}, {""val"": 381, ""count"": 15}, {""val"": 380, ""count"": 15}, {""val"": 299, ""count"": 3}, {""val"": 387, ""count"": 1}, {""val"": 388, ""count"": 1}]","[{""val"": 1200.0, ""count"": 926}, {""val"": 4800.0, ""count"": 121}]",CC0,doi:10.18112/openneuro.ds005752.v2.1.0,
ds005776,"[""Yucel2015""]",author_year,Yucel2025_Electrical,11,46,5,Tactile,Motor,Healthy,,1286928698.0,1.2 GB,openneuro,Electrical_Thermal_FingerTapping_2015,fnirs,"[{""val"": 102, ""count"": 46}]","[{""val"": 50.0, ""count"": 46}]",CC0,doi:10.18112/openneuro.ds005776.v1.0.1,
ds005777,"[""Peng2018""]",author_year,Peng2025,14,113,2,Tactile,Perception,Unknown,,906782275.0,864.8 MB,openneuro,Electrical_Morphine_Placebo_2018,fnirs,"[{""val"": 66, ""count"": 113}]","[{""val"": 25.0, ""count"": 113}]",CC0,doi:10.18112/openneuro.ds005777.v1.0.1,
ds005779,[],,Khatri2025,19,250,16,Other,Clinical/Intervention,Healthy,19.778788944444443,95206991257.0,88.7 GB,openneuro,Real-time personalized brain state-dependent TMS in healthy adults,eeg,"[{""val"": 67, ""count"": 235}, {""val"": 64, ""count"": 14}, {""val"": 70, ""count"": 1}]","[{""val"": 5000.0, ""count"": 250}]",CC0,doi:10.18112/openneuro.ds005779.v1.0.1,
ds005795,[],,Stadler2025,34,39,2,Auditory,Learning,Healthy,7.933313888888889,6887944978.0,6.4 GB,openneuro,MULTI-CLARID (Multimodal Category Learning and Resting-state Imaging Data),eeg,"[{""val"": 72, ""count"": 39}]","[{""val"": 500.0, ""count"": 39}]",CC0,doi:10.18112/openneuro.ds005795.v1.0.0,
ds005810,"[""NOD_MEG""]",canonical,Zhang2025_MEG,31,305,2,Visual,Perception,Healthy,25.52289236111111,191777285350.0,178.6 GB,openneuro,NOD-MEG,meg,"[{""val"": 409, ""count"": 285}, {""val"": 378, ""count"": 20}]","[{""val"": 1200.0, ""count"": 305}]",CC0,doi:10.18112/openneuro.ds005810.v2.0.0,
ds005811,"[""NOD_EEG""]",canonical,Zhang2025_EEG,19,448,1,Visual,Perception,Healthy,23.7022,17432462486.0,16.2 GB,openneuro,NOD-EEG,eeg,"[{""val"": 64, ""count"": 440}, {""val"": 66, ""count"": 8}]","[{""val"": 500.0, ""count"": 288}, {""val"": 1000.0, ""count"": 160}]",CC0,doi:10.18112/openneuro.ds005811.v1.0.9,
ds005815,[],,Chang2025,20,103,3,Multisensory,Perception,Healthy,3.9896991666666666,8155837338.0,7.6 GB,openneuro,"A Human EEG
Dataset for Multisensory Perception and Mental
Imagery",eeg,"[{""val"": 31, ""count"": 103}]","[{""val"": 1000.0, ""count"": 103}]",CC0,doi:10.18112/openneuro.ds005815.v2.0.1,
ds005841,[],,Karakashevska2025,48,288,6,Visual,Perception,Healthy,21.566111111111113,7889278693.0,7.3 GB,openneuro,EEG Experiment measuring ERPs in VR,eeg,"[{""val"": 73, ""count"": 1}]","[{""val"": 512, ""count"": 288}]",CC0,doi:10.18112/openneuro.ds005841.v1.0.0,
ds005857,"[""Broitman2019""]",author_year,Broitman2025,29,110,1,Visual,Memory,Unknown,101.04776285807291,305355180585.0,284.4 GB,openneuro,ltpDelayRepFRReadOnly,eeg,"[{""val"": 137, ""count"": 110}]","[{""val"": 2048.0, ""count"": 110}]",CC0,doi:10.18112/openneuro.ds005857.v1.0.0,
ds005863,[],,Isbell2025_Cognitive,127,357,4,Multisensory,Other,Healthy,50.88856666666666,11371789699.0,10.6 GB,openneuro,Cognitive Electrophysiology in Socioeconomic Context in Adulthood,eeg,"[{""val"": 30, ""count"": 357}]","[{""val"": 500.0, ""count"": 357}]",CC0,doi:10.18112/openneuro.ds005863.v2.0.0,
ds005866,"[""Flankers_NEAR""]",canonical,TerhuneCotter2025_NEAR,60,60,1,Visual,Attention,Healthy,15.976248888888888,3837193012.0,3.6 GB,openneuro,Flankers-NEAR,eeg,"[{""val"": 32, ""count"": 60}]","[{""val"": 500.0, ""count"": 60}]",CC0,doi:10.18112/openneuro.ds005866.v1.0.1,
ds005868,"[""Flankers_FAR""]",canonical,TerhuneCotter2025_FAR,48,48,1,Visual,Attention,Healthy,13.093546111111111,3146044984.0,2.9 GB,openneuro,Flankers-FAR,eeg,"[{""val"": 32, ""count"": 48}]","[{""val"": 500.0, ""count"": 48}]",CC0,doi:10.18112/openneuro.ds005868.v1.0.1,
ds005872,"[""EEGEyeNet""]",canonical,Plomecka2025,1,1,1,Visual,Attention,Healthy,0.0898511111111111,41874965.0,39.9 MB,openneuro,EEGEyeNet Dataset,eeg,"[{""val"": 129, ""count"": 1}]","[{""val"": 500.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds005872.v1.0.0,
ds005873,"[""SeizeIT2""]",canonical,Bhagubai2025,125,5654,1,Other,Clinical/Intervention,Epilepsy,22897.171388888888,47635473156.0,44.4 GB,openneuro,SeizeIT2,"eeg, emg","[{""val"": 2, ""count"": 2850}, {""val"": 1, ""count"": 2804}]","[{""val"": 256.0, ""count"": 5654}]",CC0,doi:10.18112/openneuro.ds005873.v1.1.0,
ds005876,[],,Girard2025,29,29,1,Auditory,Memory,Healthy,16.017188611111113,7654276844.0,7.1 GB,openneuro,Song Familiarity,eeg,"[{""val"": 32, ""count"": 29}]","[{""val"": 1000.0, ""count"": 29}]",CC0,doi:10.18112/openneuro.ds005876.v1.0.1,
ds005907,[],,Campbell2025,53,53,1,Visual,Learning,Alcohol,14.171917222222223,6062770483.0,5.6 GB,openneuro,EEG: RL Task (3-Armed Bandit) with alcohol cues in hazardous drinkers and ctls,eeg,"[{""val"": 58, ""count"": 13}, {""val"": 57, ""count"": 13}, {""val"": 56, ""count"": 11}, {""val"": 55, ""count"": 6}, {""val"": 59, ""count"": 4}, {""val"": 54, ""count"": 2}, {""val"": 52, ""count"": 1}, {""val"": 53, ""count"": 1}, {""val"": 33, ""count"": 1}, {""val"": 61, ""count"": 1}]","[{""val"": 500.0, ""count"": 53}]",CC0,doi:10.18112/openneuro.ds005907.v1.0.0,
ds005929,"[""Yucel2014"", ""Motion_Yucel2014""]",author_year,MotionYucel2014,7,7,1,Motor,Motor,Healthy,,71847616.0,68.5 MB,openneuro,Motion-Yucel2014,fnirs,"[{""val"": 28, ""count"": 7}]","[{""val"": 50.0, ""count"": 7}]",CC0,doi:10.18112/openneuro.ds005929.v1.0.1,
ds005930,[],author_year,Gao2023,12,36,1,Motor,Motor,Unknown,,319015113.0,304.2 MB,openneuro,BallSqueezingHD_Gao2023,fnirs,"[{""val"": 200, ""count"": 36}]","[{""val"": 8.719308035714286, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds005930.v1.0.1,
ds005931,[],,Ueda2025,8,16,1,Visual,Motor,Epilepsy,1.8886666666666667,857380965.0,817.7 MB,openneuro,Visuomotor_task,ieeg,"[{""val"": 128, ""count"": 12}, {""val"": 112, ""count"": 2}, {""val"": 110, ""count"": 2}]","[{""val"": 1000.0, ""count"": 16}]",CC0,doi:10.18112/openneuro.ds005931.v1.0.0,
ds005932,"[""PWIe""]",canonical,Holcomb2025,29,29,1,Visual,Other,Healthy,9.946916666666667,2446718661.0,2.3 GB,openneuro,PWIe,eeg,"[{""val"": 32, ""count"": 29}]","[{""val"": 500.0, ""count"": 29}]",CC0,doi:10.18112/openneuro.ds005932.v1.0.0,
ds005935,[],author_year,Li2025,21,64,1,Visual,Motor,Unknown,,774469713.0,738.6 MB,openneuro,Mirror Neuron Study,fnirs,"[{""val"": 120, ""count"": 64}]","[{""val"": 25.0, ""count"": 63}, {""val"": 25.000000000000004, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds005935.v1.0.0,
ds005946,"[""PROMENADE""]",canonical,Frau2025,39,39,1,Visual,Perception,Healthy,18.3456,15923179540.0,14.8 GB,openneuro,ERC_CoG PROMENADE - WP2 - MetaImagery (Metaphor and Mental Imagery),eeg,"[{""val"": 60, ""count"": 39}]","[{""val"": 1000.0, ""count"": 39}]",CC0,doi:10.18112/openneuro.ds005946.v1.0.1,
ds005953,[],,Winawer2025,2,3,1,Visual,Perception,Surgery,0.19469887949170006,605346653.0,577.3 MB,openneuro,iEEG_visual,ieeg,"[{""val"": 96, ""count"": 2}, {""val"": 118, ""count"": 1}]","[{""val"": 1525.9, ""count"": 2}, {""val"": 3051.76, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds005953.v1.0.0,
ds005960,[],,Pena2025,41,41,1,Visual,Attention,Healthy,66.90505555555556,61945670760.0,57.7 GB,openneuro,General Info: inst-comp-eeg,eeg,"[{""val"": 63, ""count"": 41}]","[{""val"": 1000.0, ""count"": 41}]",CC0,doi:10.18112/openneuro.ds005960.v1.0.0,
ds005963,"[""Mesquita2019""]",author_year,Mesquita2025,10,40,1,Unknown,Motor,Unknown,6.489715555555555,244698664.0,233.4 MB,openneuro,FRESH Motor Dataset,fnirs,"[{""val"": 136, ""count"": 40}]","[{""val"": 8.928571428571429, ""count"": 40}]",CC0,doi:10.18112/openneuro.ds005963.v1.0.0,
ds005964,"[""Luke2019""]",author_year,Luke2025,17,17,1,Auditory,Perception,Unknown,6.14368,65461433.0,62.4 MB,openneuro,FRESH Audio Dataset,fnirs,"[{""val"": 66, ""count"": 17}]","[{""val"": 5.208333333333333, ""count"": 17}]",CC0,doi:10.18112/openneuro.ds005964.v1.0.0,
ds006012,[],,SableMeyer2025,21,193,2,Visual,Perception,Healthy,15.586618611111112,76309397809.0,71.1 GB,openneuro,A geometric shape regularity effect in the human brain: MEG dataset,meg,"[{""val"": 336, ""count"": 172}, {""val"": 333, ""count"": 1}]","[{""val"": 1000.0, ""count"": 173}]",CC0,doi:10.18112/openneuro.ds006012.v1.0.1,
ds006018,[],,Isbell2025_Adulthood,127,357,4,Multisensory,Other,Healthy,50.88856666666666,11372008786.0,10.6 GB,openneuro,Cognitive Electrophysiology in Socioeconomic Context in Adulthood: An EEG dataset,eeg,"[{""val"": 30, ""count"": 357}]","[{""val"": 500.0, ""count"": 357}]",CC0,doi:10.18112/openneuro.ds006018.v1.2.2,
ds006033,[],,Liwicki2025,3,5,1,Visual,Other,Healthy,2.1883706111111114,16449271220.0,15.3 GB,openneuro,Synchronous EEG and fMRI dataset on inner speech,eeg,"[{""val"": 66, ""count"": 5}]","[{""val"": 5000.0, ""count"": 5}]",CC0,doi:10.18112/openneuro.ds006033.v1.0.1,
ds006035,"[""Lin2019""]",author_year,Lin2025,5,15,1,Tactile,Motor,Healthy,1.1343288512795158,3295034095.0,3.1 GB,openneuro,somatomotor,meg,"[{""val"": 388, ""count"": 12}, {""val"": 387, ""count"": 3}]","[{""val"": 1004.01611328125, ""count"": 15}]",CC0,doi:10.18112/openneuro.ds006035.v1.0.0,
ds006036,[],,Ntetska2025,88,88,1,Visual,Clinical/Intervention,Dementia,7.716666666666667,1125336677.0,1.0 GB,openneuro,"A complementary dataset of open-eyes EEG recordings in a photo-stimulation setting from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects",eeg,"[{""val"": 19, ""count"": 88}]","[{""val"": 500.0, ""count"": 88}]",CC0,doi:10.18112/openneuro.ds006036.v1.0.6,
ds006040,[],,Cha2025,28,392,10,Visual,Other,Healthy,,185217929633.0,172.5 GB,openneuro,Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI,eeg,"[{""val"": 64, ""count"": 391}]","[{""val"": 5000.0, ""count"": 391}]",CC0,doi:10.18112/openneuro.ds006040.v1.0.2,
ds006065,[],author_year,Kragel2025,7,45,10,Other,Clinical/Intervention,Surgery,10.699191111111112,10343281171.0,9.6 GB,openneuro,TSS_iEEG,ieeg,"[{""val"": 168, ""count"": 15}, {""val"": 175, ""count"": 10}, {""val"": 82, ""count"": 5}, {""val"": 68, ""count"": 5}, {""val"": 181, ""count"": 5}, {""val"": 43, ""count"": 5}]","[{""val"": 500.0, ""count"": 45}]",CC0,doi:10.18112/openneuro.ds006065.v1.0.0,
ds006095,[],,Liu2025_Mind_Motion_Older,71,1182,9,Motor,Motor,Healthy,61.09685555555556,139411715731.0,129.8 GB,openneuro,Mind in Motion Older Adults Walking Over Uneven Terrain,eeg,"[{""val"": 284, ""count"": 1053}, {""val"": 310, ""count"": 115}, {""val"": 336, ""count"": 14}]","[{""val"": 500.0, ""count"": 1182}]",CC0,doi:10.18112/openneuro.ds006095.v1.0.0,
ds006104,[],,Moreira2025,24,56,3,Auditory,Perception,Healthy,50.75694444444444,46128446170.0,43.0 GB,openneuro,EEG dataset for speech decoding,eeg,"[{""val"": 61, ""count"": 53}, {""val"": 83, ""count"": 3}]","[{""val"": 2000.0, ""count"": 56}]",CC0,doi:10.18112/openneuro.ds006104.v1.0.1,
ds006107,"[""Kuroda2024""]",author_year,Kuroda2025,166,167,1,Sleep,Sleep,Unknown,16.50111111111111,12778446176.0,11.9 GB,openneuro,iEEG_Neural_spatial_volatility,ieeg,"[{""val"": 128, ""count"": 30}, {""val"": 112, ""count"": 19}, {""val"": 104, ""count"": 7}, {""val"": 108, ""count"": 6}, {""val"": 118, ""count"": 6}, {""val"": 124, ""count"": 5}, {""val"": 102, ""count"": 5}, {""val"": 132, ""count"": 5}, {""val"": 100, ""count"": 5}, {""val"": 120, ""count"": 5}, {""val"": 106, ""count"": 5}, {""val"": 138, ""count"": 4}, {""val"": 130, ""count"": 4}, {""val"": 58, ""count"": 4}, {""val"": 140, ""count"": 3}, {""val"": 110, ""count"": 3}, {""val"": 116, ""count"": 3}, {""val"": 34, ""count"": 2}, {""val"": 86, ""count"": 2}, {""val"": 136, ""count"": 2}, {""val"": 150, ""count"": 2}, {""val"": 84, ""count"": 2}, {""val"": 114, ""count"": 2}, {""val"": 64, ""count"": 2}, {""val"": 126, ""count"": 2}, {""val"": 144, ""count"": 2}, {""val"": 72, ""count"": 2}, {""val"": 98, ""count"": 2}, {""val"": 48, ""count"": 1}, {""val"": 78, ""count"": 1}, {""val"": 68, ""count"": 1}, {""val"": 94, ""count"": 1}, {""val"": 80, ""count"": 1}, {""val"": 44, ""count"": 1}, {""val"": 134, ""count"": 1}, {""val"": 73, ""count"": 1}, {""val"": 70, ""count"": 1}, {""val"": 52, ""count"": 1}, {""val"": 109, ""count"": 1}, {""val"": 156, ""count"": 1}, {""val"": 88, ""count"": 1}, {""val"": 28, ""count"": 1}, {""val"": 74, ""count"": 1}, {""val"": 69, ""count"": 1}, {""val"": 38, ""count"": 1}, {""val"": 164, ""count"": 1}, {""val"": 82, ""count"": 1}, {""val"": 56, ""count"": 1}, {""val"": 96, ""count"": 1}, {""val"": 54, ""count"": 1}, {""val"": 133, ""count"": 1}, {""val"": 90, ""count"": 1}, {""val"": 46, ""count"": 1}, {""val"": 122, ""count"": 1}]","[{""val"": 1000.0, ""count"": 167}]",CC0,doi:10.18112/openneuro.ds006107.v1.0.0,
ds006126,[],,Mensah2025,5,90,6,Motor,Motor,Healthy,10.730372611111111,1159689297.0,1.1 GB,openneuro,TDCS Modulation of Visual Cortex in Motor Imagery,eeg,"[{""val"": 3, ""count"": 90}]","[{""val"": 5000.0, ""count"": 90}]",CC0,doi:10.18112/openneuro.ds006126.v1.0.0,
ds006136,"[""Omelyusik2026""]",author_year,Omelyusik2025,13,14,1,Visual,Memory,Epilepsy,25.273888888888887,299778229.0,285.9 MB,openneuro,OWM-Dataset,ieeg,"[{""val"": 8, ""count"": 2}, {""val"": 7, ""count"": 2}, {""val"": 9, ""count"": 2}, {""val"": 12, ""count"": 2}, {""val"": 18, ""count"": 1}, {""val"": 6, ""count"": 1}, {""val"": 14, ""count"": 1}, {""val"": 17, ""count"": 1}, {""val"": 5, ""count"": 1}, {""val"": 11, ""count"": 1}]","[{""val"": 1000.0, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds006136.v1.0.1,
ds006142,[],,MatranFernandez2025,27,27,1,Visual,Memory,Healthy,26.295897216796874,26073650133.0,24.3 GB,openneuro,Essex EEG Movie Memory dataset,eeg,"[{""val"": 65, ""count"": 27}]","[{""val"": 2048.0, ""count"": 27}]",CC0,doi:10.18112/openneuro.ds006142.v1.0.2,
ds006159,"[""LeganesFonteneau2024""]",author_year,LeganesFonteneau2025,61,61,1,Unknown,Learning,Healthy,14.299166666666666,15404183606.0,14.3 GB,openneuro,Implicit Learning EEG (BioSemi),eeg,"[{""val"": 73, ""count"": 14}]","[{""val"": 1024.0, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds006159.v1.0.0,
ds006171,"[""Melcon2024""]",author_year,Melcon2025,36,104,3,Visual,Attention,Healthy,40.88305555555556,72833078851.0,67.8 GB,openneuro,"EEG data during three near-threshold visual detection tasks: a no-cue task, a noninformative cue task (50% validity), and an informative cue task (100% validity)",eeg,"[{""val"": 144, ""count"": 104}]","[{""val"": 1024.0, ""count"": 104}]",CC0,doi:10.18112/openneuro.ds006171.v1.0.0,
ds006222,[],,Attokaren2025,69,70,1,Multisensory,Attention,Healthy,52.80083333333334,15825503728.0,14.7 GB,openneuro,MultisensoryFlickerHealthyYoungAdults_AllSubjectsRawData,eeg,"[{""val"": 40, ""count"": 70}]","[{""val"": 512.0, ""count"": 70}]",CC0,doi:10.18112/openneuro.ds006222.v1.0.1,
ds006233,[],,Kochi2025_Picture_naming,108,347,1,Visual,Other,Surgery,,18579703387.0,17.3 GB,openneuro,Picture naming,ieeg,"[{""val"": 128, ""count"": 245}, {""val"": 138, ""count"": 19}, {""val"": 136, ""count"": 19}, {""val"": 140, ""count"": 8}, {""val"": 110, ""count"": 6}, {""val"": 112, ""count"": 6}, {""val"": 150, ""count"": 5}, {""val"": 156, ""count"": 5}, {""val"": 134, ""count"": 4}, {""val"": 148, ""count"": 4}, {""val"": 130, ""count"": 4}, {""val"": 164, ""count"": 4}, {""val"": 96, ""count"": 3}, {""val"": 152, ""count"": 3}, {""val"": 144, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 118, ""count"": 3}, {""val"": 64, ""count"": 2}, {""val"": 58, ""count"": 1}]","[{""val"": 1000.0, ""count"": 347}]",CC0,doi:10.18112/openneuro.ds006233.v1.0.0,
ds006234,[],author_year,Kochi2025_Auditory_naming,119,378,1,Auditory,Other,Surgery,128.09192777777778,47167316634.0,43.9 GB,openneuro,Auditory naming,ieeg,"[{""val"": 128, ""count"": 269}, {""val"": 138, ""count"": 14}, {""val"": 136, ""count"": 11}, {""val"": 112, ""count"": 9}, {""val"": 140, ""count"": 8}, {""val"": 164, ""count"": 8}, {""val"": 134, ""count"": 7}, {""val"": 110, ""count"": 6}, {""val"": 142, ""count"": 5}, {""val"": 156, ""count"": 5}, {""val"": 150, ""count"": 5}, {""val"": 132, ""count"": 4}, {""val"": 130, ""count"": 4}, {""val"": 148, ""count"": 4}, {""val"": 144, ""count"": 4}, {""val"": 152, ""count"": 3}, {""val"": 118, ""count"": 3}, {""val"": 96, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 64, ""count"": 2}, {""val"": 58, ""count"": 1}]","[{""val"": 1000.0, ""count"": 378}]",CC0,doi:10.18112/openneuro.ds006234.v1.0.0,
ds006253,"[""MetaRDK""]",canonical,Goueytes2024,23,201,4,Visual,Decision-making,Epilepsy,,671939.0,656.2 KB,openneuro,MetaRDK,ieeg,"[{""val"": 122, ""count"": 13}, {""val"": 185, ""count"": 2}, {""val"": 120, ""count"": 1}, {""val"": 143, ""count"": 1}, {""val"": 156, ""count"": 1}, {""val"": 186, ""count"": 1}, {""val"": 201, ""count"": 1}, {""val"": 132, ""count"": 1}]",[],CC0,doi:10.18112/openneuro.ds006253.v1.0.3,
ds006260,[],,CoronaGonzalez2025,76,366,1,Visual,Clinical/Intervention,Development,23.128793402777777,2908201731.0,2.7 GB,openneuro,Dataset of psychophysiological data from children with learning difficulties who strengthen reading and math skills through assistive technology,eeg,"[{""val"": 32, ""count"": 366}]","[{""val"": 256.0, ""count"": 366}]",CC0,doi:10.18112/openneuro.ds006260.v1.0.1,
ds006269,[],,Pritchard2025,24,40,2,Resting State,Resting-state,Other,240.0,114536455305.0,106.7 GB,openneuro,Tethered EEG Recordings in Syngap1 rats,eeg,"[{""val"": 33, ""count"": 40}]","[{""val"": 1000.0, ""count"": 40}]",CC0,doi:10.18112/openneuro.ds006269.v1.0.0,
ds006317,"[""Chisco2_0"", ""Chisco20"", ""CHISCO20""]",canonical,Zhang2025_Chisco_2_0,2,64,2,Unknown,Motor,Healthy,62.12170444444445,56822054258.0,52.9 GB,openneuro,Chisco-2.0,eeg,"[{""val"": 127, ""count"": 64}]","[{""val"": 1000.0, ""count"": 64}]",CC0,doi:10.18112/openneuro.ds006317.v1.1.0,
ds006334,[],author_year,Biau2025,30,128,1,Multisensory,Memory,Healthy,36.74722222222222,178454548010.0,166.2 GB,openneuro,Neocortical and Hippocampal Theta Oscillations Track Audiovisual Integration and Replay of Speech Memories,meg,"[{""val"": 331, ""count"": 74}, {""val"": 332, ""count"": 54}]","[{""val"": 1000.0, ""count"": 128}]",CC0,doi:10.18112/openneuro.ds006334.v1.0.0,
ds006366,"[""MSSV""]",canonical,Rose2025,92,148,1,Sleep,Sleep,Healthy,2163.2877777777776,6526725895.0,6.1 GB,openneuro,Mouse Sleep Staging Validation dataset (MSSV),eeg,"[{""val"": 3, ""count"": 71}, {""val"": 2, ""count"": 43}, {""val"": 5, ""count"": 34}]","[{""val"": 128.0, ""count"": 148}]",CC0,doi:10.18112/openneuro.ds006366.v1.0.1,
ds006367,[],,DS6367_Memory_Reactivation,52,52,1,Visual,Memory,Healthy,68.17161055555555,29810755955.0,27.8 GB,openneuro,Memory Reactivation Levels Remain Unaffected by Anticipated Interference,eeg,"[{""val"": 30, ""count"": 52}]","[{""val"": 1000.0, ""count"": 52}]",CC0,doi:10.18112/openneuro.ds006367.v1.0.1,
ds006370,[],,DS6370_Memory_Reactivation,56,56,1,Visual,Memory,Healthy,95.32295833333335,43035741246.0,40.1 GB,openneuro,Memory Reactivation Levels Remain Unaffected by Anticipated Interference Experiment 2 Dataset,eeg,"[{""val"": 30, ""count"": 56}]","[{""val"": 1000.0, ""count"": 56}]",CC0,doi:10.18112/openneuro.ds006370.v1.0.1,
ds006374,"[""Pohle2019""]",author_year,Pohle2025,36,358,2,Tactile,Perception,Healthy,31.611037916666668,33413497382.0,31.1 GB,openneuro,Expectation effects on repetition suppression in nociception,eeg,"[{""val"": 35, ""count"": 358}]","[{""val"": 2000.0, ""count"": 358}]",CC0,doi:10.18112/openneuro.ds006374.v1.0.0,
ds006377,[],,Yucel2025_InclusionStudy,115,690,6,Motor,Motor,Unknown,,1480974514.0,1.4 GB,openneuro,InclusionStudy,fnirs,"[{""val"": 52, ""count"": 690}]","[{""val"": 10.172526041666666, ""count"": 643}, {""val"": 10.172526041666664, ""count"": 24}, {""val"": 10.172526041666668, ""count"": 21}, {""val"": 10.172526043256445, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds006377.v1.0.2,
ds006386,"[""Yu2019""]",author_year,Yu2025,30,180,1,Motor,Other,Healthy,57.99939444444445,24666877908.0,23.0 GB,openneuro,PhysioMotion_Artifact,eeg,"[{""val"": 59, ""count"": 180}]","[{""val"": 1000.0, ""count"": 180}]",CC0,doi:10.18112/openneuro.ds006386.v1.0.1,
ds006392,"[""Hermes2024""]",author_year,Attia2025,1,1,1,Visual,Perception,Unknown,0.24034288194444445,33603727.0,32.0 MB,openneuro,HED schema library for SCORE annotations example,ieeg,"[{""val"": 166, ""count"": 1}]","[{""val"": 512.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds006392.v1.0.1,
ds006394,[],,Leong2025,33,60,2,Multisensory,Attention,Healthy,,560818153.0,534.8 MB,openneuro,Electrophysiological markers of surprise-induced failures of visual and auditory awareness,eeg,"[{""val"": 16, ""count"": 60}]","[{""val"": 125.0, ""count"": 60}]",CC0,doi:10.18112/openneuro.ds006394.v1.0.3,
ds006434,[],,Stoll2025,66,118,5,Auditory,Attention,Healthy,263.13554426111114,110646361086.0,103.0 GB,openneuro,The auditory brainstem response to natural speech is not affected by selective attention,eeg,"[{""val"": 32, ""count"": 52}, {""val"": 2, ""count"": 28}, {""val"": 1, ""count"": 24}, {""val"": 3, ""count"": 14}]","[{""val"": 10000.0, ""count"": 52}, {""val"": 1000.0, ""count"": 28}, {""val"": 500.0, ""count"": 24}, {""val"": 25000.0, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds006434.v1.2.0,
ds006437,[],,DS6437_LIGHT_Hypnotherapy,9,63,5,Auditory,Clinical/Intervention,Healthy,16.798037109375,4631684039.0,4.3 GB,openneuro,LIGHT Hypnotherapy,eeg,"[{""val"": 64, ""count"": 63}]","[{""val"": 256.0, ""count"": 63}]",CC0,doi:10.18112/openneuro.ds006437.v1.1.0,
ds006446,"[""Kinley2019""]",author_year,Kinley2025,29,29,1,Visual,Decision-making,Healthy,18.029718288845487,17285482171.0,16.1 GB,openneuro,Cueing the future to reduce temporal discounting,eeg,"[{""val"": 65, ""count"": 29}]","[{""val"": 2048.0, ""count"": 29}]",CC0,doi:10.18112/openneuro.ds006446.v1.0.0,
ds006459,[],author_year,Anderson2025_Sparse,17,17,1,Visual,Attention,Healthy,,176977615.0,168.8 MB,openneuro,High-DensityvSparsefNIRS_WordColorStroop_Sparse_Anderson_2025,fnirs,"[{""val"": 120, ""count"": 17}]","[{""val"": 24.414062499999996, ""count"": 15}, {""val"": 24.414062499999993, ""count"": 1}, {""val"": 24.414062500000004, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds006459.v1.0.0,
ds006460,[],author_year,Anderson2025_HD,17,17,1,Visual,Attention,Healthy,,482075463.0,459.7 MB,openneuro,High-DensityvSparsefNIRS_WordColorStroop_HD_Anderson_2025,fnirs,"[{""val"": 428, ""count"": 17}]","[{""val"": 17.438616071428573, ""count"": 15}, {""val"": 17.438616071428577, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds006460.v1.0.0,
ds006465,"[""CPSEED_3M"", ""CPSEED""]",canonical,Ma2025,20,80,1,Visual,Motor,Healthy,36.199955555555555,8768486080.0,8.2 GB,openneuro,"3M-CPSEED：An EEG-based Dataset for Chinese Pinyin Production in Overt, Silent-intended, and Imagined Speech",eeg,"[{""val"": 32, ""count"": 58}, {""val"": 126, ""count"": 19}, {""val"": 33, ""count"": 3}]","[{""val"": 500.0, ""count"": 80}]",CC0,doi:10.18112/openneuro.ds006465.v2.0.0,
ds006466,"[""HeartBEAM""]",canonical,Kim2025_HeartBEAM_Older_Adult,66,1257,6,Auditory,Attention,Healthy,131.76201944444446,126131448388.0,117.5 GB,openneuro,HeartBEAM: Older Adult Resting State and Auditory Oddball Task EEG Data,eeg,"[{""val"": 65, ""count"": 1257}]","[{""val"": 1000.0, ""count"": 1257}]",CC0,doi:10.18112/openneuro.ds006466.v1.0.1,
ds006468,"[""MEG_SCANS""]",canonical,Habersetzer2025,24,189,4,Auditory,Perception,Healthy,21.73856527777778,108674320301.0,101.2 GB,openneuro,"MEG-SCANS - A comprehensive magnetoencephalography speech dataset with Stories, Chirps And Noisy Sentences.",meg,"[{""val"": 341, ""count"": 153}, {""val"": 347, ""count"": 7}, {""val"": 372, ""count"": 5}]","[{""val"": 1000.0, ""count"": 165}]",CC0,doi:10.18112/openneuro.ds006468.v1.1.2,
ds006480,[],,Kim2025_Young_Adult_Resting,68,68,1,Auditory,Attention,Healthy,71.66592555555556,68786099468.0,64.1 GB,openneuro,Young Adult Resting State and Auditory Oddball Task EEG Data,eeg,"[{""val"": 65, ""count"": 68}]","[{""val"": 1000.0, ""count"": 68}]",CC0,doi:10.18112/openneuro.ds006480.v1.0.1,
ds006502,[],,Bonstrup2025,31,380,4,Visual,Learning,Healthy,37.88333333333333,102812849034.0,95.8 GB,openneuro,Skill learning and consolidation in healthy humans,meg,"[{""val"": 307, ""count"": 204}, {""val"": 308, ""count"": 101}, {""val"": 310, ""count"": 51}, {""val"": 306, ""count"": 24}]","[{""val"": 600.0, ""count"": 380}]",CC0,doi:10.18112/openneuro.ds006502.v1.0.0,
ds006519,[],,Barborica2025,21,35,1,Other,Clinical/Intervention,Epilepsy,0.26861111111111113,1118742821.0,1.0 GB,openneuro,Dataset of intracranial EEG during cortical stimulations evoking negative motor responses,ieeg,"[{""val"": 35, ""count"": 5}, {""val"": 33, ""count"": 5}, {""val"": 37, ""count"": 4}, {""val"": 31, ""count"": 2}, {""val"": 32, ""count"": 2}, {""val"": 41, ""count"": 2}, {""val"": 52, ""count"": 2}, {""val"": 61, ""count"": 1}, {""val"": 176, ""count"": 1}, {""val"": 56, ""count"": 1}, {""val"": 63, ""count"": 1}, {""val"": 25, ""count"": 1}, {""val"": 150, ""count"": 1}, {""val"": 40, ""count"": 1}, {""val"": 34, ""count"": 1}, {""val"": 43, ""count"": 1}, {""val"": 89, ""count"": 1}, {""val"": 101, ""count"": 1}, {""val"": 69, ""count"": 1}, {""val"": 47, ""count"": 1}]","[{""val"": 4096.0, ""count"": 31}, {""val"": 512.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds006519.v1.0.0,
ds006525,[],,Neuroimaging2025,34,34,1,Resting State,Resting-state,Unknown,,3223158965.0,3.0 GB,openneuro,Resting EEG,eeg,"[{""val"": 128, ""count"": 26}, {""val"": 129, ""count"": 8}]","[{""val"": 250.0, ""count"": 34}]",CC0,doi:10.18112/openneuro.ds006525.v1.0.0,
ds006545,"[""Dubois2024""]",author_year,ReliabilityDubois2024,49,98,1,Auditory,Unknown,Unknown,,50147809917.0,46.7 GB,openneuro,Reliability-Dubois2024,fnirs,"[{""val"": 6180, ""count"": 2}, {""val"": 6498, ""count"": 2}, {""val"": 8340, ""count"": 2}, {""val"": 3678, ""count"": 2}, {""val"": 6708, ""count"": 2}, {""val"": 12180, ""count"": 2}, {""val"": 6990, ""count"": 1}, {""val"": 6696, ""count"": 1}, {""val"": 5400, ""count"": 1}, {""val"": 6282, ""count"": 1}, {""val"": 4614, ""count"": 1}, {""val"": 6432, ""count"": 1}, {""val"": 11682, ""count"": 1}, {""val"": 4170, ""count"": 1}, {""val"": 9558, ""count"": 1}, {""val"": 5640, ""count"": 1}, {""val"": 7890, ""count"": 1}, {""val"": 4752, ""count"": 1}, {""val"": 7794, ""count"": 1}, {""val"": 5076, ""count"": 1}, {""val"": 12300, ""count"": 1}, {""val"": 5676, ""count"": 1}, {""val"": 3552, ""count"": 1}, {""val"": 12738, ""count"": 1}, {""val"": 8730, ""count"": 1}, {""val"": 9012, ""count"": 1}, {""val"": 5280, ""count"": 1}, 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ds006547,"[""Ghaffari2024""]",author_year,Ghaffari2025,31,31,1,Visual,Perception,Healthy,39.21989444444444,18921502382.0,17.6 GB,openneuro,Visual EEG Study (BrainVision → BIDS),eeg,"[{""val"": 64, ""count"": 31}]","[{""val"": 500.0, ""count"": 31}]",CC0,doi:10.18112/openneuro.ds006547.v1.0.0,
ds006554,[],,Su2025,47,47,1,Unknown,Unknown,Unknown,28.228528333333333,13014195680.0,12.1 GB,openneuro,Social Observation EEG raw data,eeg,"[{""val"": 64, ""count"": 47}]","[{""val"": 500.0, ""count"": 47}]",CC0,doi:10.18112/openneuro.ds006554.v1.0.0,
ds006563,[],,Gramann2025,12,12,1,Visual,Attention,Healthy,12.639361666666666,6045373165.0,5.6 GB,openneuro,Dimension-based attention modulates early visual processing,eeg,"[{""val"": 64, ""count"": 12}]","[{""val"": 500.0, ""count"": 12}]",CC0,doi:10.18112/openneuro.ds006563.v1.0.0,
ds006576,[],,McDevitt2025,57,57,1,Sleep,Sleep,Healthy,97.67361111111111,594736641777.0,553.9 GB,openneuro,The role of REM sleep in neural differentiation of memories in the hippocampus,eeg,"[{""val"": 73, ""count"": 57}]","[{""val"": 512.0, ""count"": 57}]",CC0,doi:10.18112/openneuro.ds006576.v1.0.3,
ds006593,[],,Celik2025,21,21,1,Visual,Attention,Healthy,5.610982407407407,463412434.0,441.9 MB,openneuro,cBCI Matrix Multimodal Dataset,eeg,"[{""val"": 19, ""count"": 21}]","[{""val"": 300.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds006593.v1.0.0,
ds006629,"[""SINGSING""]",canonical,Chanoine2025,19,38,2,Auditory,Perception,Healthy,,12055724574.0,11.2 GB,openneuro,SINGSING,meg,"[{""val"": 339, ""count"": 38}]","[{""val"": 250.0, ""count"": 38}]",CC0,doi:10.18112/openneuro.ds006629.v1.0.1,
ds006647,[],,Chaudhuri2025_D2,4,4,1,Visual,Affect,Healthy,8.681111111111111,4610198011.0,4.3 GB,openneuro,Poetry Assessment EEG Dataset 2,eeg,"[{""val"": 70, ""count"": 4}]","[{""val"": 512.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds006647.v1.0.1,
ds006648,[],,Chaudhuri2025_D1,47,47,1,Visual,Affect,Healthy,91.80333333333333,48749259066.0,45.4 GB,openneuro,Poetry Assessment EEG Dataset 1,eeg,"[{""val"": 70, ""count"": 47}]","[{""val"": 512.0, ""count"": 47}]",CC0,doi:10.18112/openneuro.ds006648.v1.0.0,
ds006673,[],author_year,Carlton2025,17,67,2,Motor,Motor,Healthy,,8349792794.0,7.8 GB,openneuro,ball_squeeze_Carlton_2025,fnirs,"[{""val"": 2440, ""count"": 52}, {""val"": 1134, ""count"": 15}]",[],CC0,doi:10.18112/openneuro.ds006673.v1.0.2,
ds006695,"[""Onton2024""]",author_year,Onton2025,19,19,1,Sleep,Sleep,Healthy,164.257975,10052862715.0,9.4 GB,openneuro,Validation of Sleep Staging with Forehead EEG Patch,eeg,"[{""val"": 3, ""count"": 19}]","[{""val"": 500.0, ""count"": 19}]",CC0,doi:10.18112/openneuro.ds006695.v1.0.2,
ds006720,[],,Herbst2025,24,246,3,Auditory,Memory,Healthy,30.722993750000004,146513556741.0,136.5 GB,openneuro,Alpha power indexes working memory load for durations,meg,"[{""val"": 328, ""count"": 209}, {""val"": 321, ""count"": 11}, {""val"": 340, ""count"": 2}, {""val"": 390, ""count"": 1}]","[{""val"": 1000.0, ""count"": 222}, {""val"": 2000.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds006720.v1.0.0,
ds006735,[],,Shan2025,27,27,1,Auditory,Perception,Healthy,38.84182222222222,188843790172.0,175.9 GB,openneuro,Chimeric music reveals an interaction of pitch and time in electrophysiological signatures of music encoding,eeg,"[{""val"": 36, ""count"": 24}, {""val"": 63, ""count"": 2}, {""val"": 34, ""count"": 1}]","[{""val"": 10000.0, ""count"": 27}]",CC0,doi:10.18112/openneuro.ds006735.v2.0.0,
ds006761,[],,Moerel2025_Neural,31,31,1,Visual,Decision-making,Healthy,25.621111111111112,83755152274.0,78.0 GB,openneuro,Neural decoding of competitive decision-making in Rock-Paper-Scissors,eeg,"[{""val"": 64, ""count"": 31}]","[{""val"": 2048.0, ""count"": 31}]",CC0,doi:10.18112/openneuro.ds006761.v1.0.0,
ds006768,[],,Lowe2025,30,210,1,Visual,Attention,Healthy,14.466388888888888,7002555913.0,6.5 GB,openneuro,Multiple Object Monitoring (EEG),eeg,"[{""val"": 64, ""count"": 210}]","[{""val"": 1000.0, ""count"": 210}]",CC0,doi:10.18112/openneuro.ds006768.v1.1.0,
ds006801,[],,Alves2025,21,42,1,Resting State,Learning,Healthy,6.335461111111111,1414488873.0,1.3 GB,openneuro,Resting-state EEG before and after different study methods,eeg,"[{""val"": 31, ""count"": 42}]","[{""val"": 500.0, ""count"": 42}]",CC0,doi:10.18112/openneuro.ds006801.v1.0.0,
ds006802,[],author_year,Moerel2025_Collaborative,24,24,1,Visual,Learning,Healthy,23.414166666666667,66817006188.0,62.2 GB,openneuro,Collaborative rule learning promotes interbrain information alignment,eeg,"[{""val"": 64, ""count"": 24}]","[{""val"": 2048.0, ""count"": 24}]",CC0,doi:10.18112/openneuro.ds006802.v1.0.0,
ds006803,[],,PechCanul2025,63,126,1,Visual,Learning,Healthy,45.946035555555554,1497259364.0,1.4 GB,openneuro,NeuroTechs Dataset for Stem Skills,eeg,"[{""val"": 8, ""count"": 126}]","[{""val"": 250.0, ""count"": 126}]",CC0,doi:10.18112/openneuro.ds006803.v1.1.1,
ds006817,"[""VisualContextTrajectory_v2"", ""Lowe2025""]",canonical,Lowe2025,34,34,1,Unknown,Unknown,Unknown,21.68111111111111,10400423631.0,9.7 GB,openneuro,Visual Attribute-Specific Contextual Trajectory Paradigm 2.0,eeg,"[{""val"": 65, ""count"": 34}]","[{""val"": 1024.0, ""count"": 34}]",CC0,doi:10.18112/openneuro.ds006817.v1.0.0,
ds006839,[],,Gonzales2025,36,144,4,Multisensory,Attention,Healthy,26.62208888888889,11119141416.0,10.4 GB,openneuro,EEG recordings during sham neurofeedback in virtual reality,eeg,"[{""val"": 29, ""count"": 144}]","[{""val"": 1000.0, ""count"": 144}]",CC0,doi:10.18112/openneuro.ds006839.v1.0.0,
ds006840,"[""IACKD""]",canonical,Cai2025,15,128,1,Motor,Motor,Healthy,14.781475694444444,6435718408.0,6.0 GB,openneuro,IACKD: Intention Action Conflict EEG-Hand Kinematics Dataset,eeg,"[{""val"": 29, ""count"": 96}, {""val"": 31, ""count"": 32}]","[{""val"": 1024.0, ""count"": 128}]",CC0,doi:10.18112/openneuro.ds006840.v1.0.0,
ds006848,[],,Kosachenko2025,30,52,2,Visual,Memory,Healthy,47.409621666666666,44416584256.0,41.4 GB,openneuro,"AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits",eeg,"[{""val"": 65, ""count"": 52}]","[{""val"": 1000.0, ""count"": 52}]",CC0,doi:10.18112/openneuro.ds006848.v1.0.0,
ds006850,[],,Zaehme2025,63,126,1,Visual,Affect,Healthy,78.82088222222221,37239047888.0,34.7 GB,openneuro,Urban Appraisal: Physiological Recording during Rating of Different Urban Environments,eeg,"[{""val"": 66, ""count"": 126}]","[{""val"": 500.0, ""count"": 126}]",CC0,doi:10.18112/openneuro.ds006850.v1.0.0,
ds006861,[],,Maka2025_Targeted,120,239,1,Visual,Affect,Healthy,99.57602750000001,55976306491.0,52.1 GB,openneuro,Targeted Neuromodulation of the Left Dorsolateral Prefrontal Cortex Alleviates Altered Affective Response Evaluation in Lonely Individuals,eeg,"[{""val"": 37, ""count"": 239}]","[{""val"": 1000.0, ""count"": 239}]",CC0,doi:10.18112/openneuro.ds006861.v1.0.2,
ds006866,[],,Maka2025_Discrepancy,148,148,1,Visual,Affect,Healthy,123.35075777777777,124810279277.0,116.2 GB,openneuro,Discrepancy between self-report and neurophysiological markers of socio-affective responses in lonely individuals,eeg,"[{""val"": 69, ""count"": 148}]","[{""val"": 1000.0, ""count"": 148}]",CC0,doi:10.18112/openneuro.ds006866.v1.0.0,
ds006890,[],,Yang2025_Longitudinal,2,870,5,Multisensory,Motor,Healthy,105.82138916666666,44207054000.0,41.2 GB,openneuro,Longitudinal Multitask Wireless ECoG Data from Two Fully Implanted Macaca fuscata,ieeg,"[{""val"": 50, ""count"": 471}, {""val"": 66, ""count"": 399}]","[{""val"": 1000.0, ""count"": 870}]",CC0,doi:10.18112/openneuro.ds006890.v1.0.0,
ds006902,[],,Geisler2025,42,42,1,Motor,Perception,Healthy,27.677904833333333,5914166447.0,5.5 GB,openneuro,Profound neuronal differences during Exercise-Induced Hypoalgesia between athletes and non-athletes revealed by functional near-infrared spectroscopy,fnirs,"[{""val"": 112, ""count"": 42}]","[{""val"": 7.627765064836003, ""count"": 42}]",CC0,doi:10.18112/openneuro.ds006902.v1.1.1,
ds006903,[],,here2025,17,67,2,Motor,Motor,Healthy,,5826262353.0,5.4 GB,openneuro,ball_squeeze_2025,fnirs,"[{""val"": 1134, ""count"": 66}, {""val"": 2440, ""count"": 1}]","[{""val"": 4.324324324324325, ""count"": 38}, {""val"": 8.98876404494382, ""count"": 15}, {""val"": 4.324324324324324, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds006903.v1.0.0,
ds006910,[],author_year,Kochi2025_Auditory_Naming_EC,121,384,1,Auditory,Other,Unknown,130.07328611111112,47891802796.0,44.6 GB,openneuro,Auditory Naming EC,ieeg,"[{""val"": 128, ""count"": 269}, {""val"": 138, ""count"": 14}, {""val"": 136, ""count"": 11}, {""val"": 112, ""count"": 9}, {""val"": 140, ""count"": 8}, {""val"": 164, ""count"": 8}, {""val"": 134, ""count"": 7}, {""val"": 110, ""count"": 6}, {""val"": 142, ""count"": 5}, {""val"": 156, ""count"": 5}, {""val"": 150, ""count"": 5}, {""val"": 132, ""count"": 4}, {""val"": 148, ""count"": 4}, {""val"": 144, ""count"": 4}, {""val"": 130, ""count"": 4}, {""val"": 160, ""count"": 3}, {""val"": 154, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 118, ""count"": 3}, {""val"": 96, ""count"": 3}, {""val"": 152, ""count"": 3}, {""val"": 64, ""count"": 2}, {""val"": 58, ""count"": 1}]","[{""val"": 1000.0, ""count"": 384}]",CC0,doi:10.18112/openneuro.ds006910.v1.0.1,
ds006914,[],,Kochi2025_Visual_Naming_EC,110,353,1,Visual,Other,Epilepsy,0.7271305555555556,18843484223.0,17.5 GB,openneuro,Visual Naming EC,ieeg,"[{""val"": 128, ""count"": 245}, {""val"": 136, ""count"": 19}, {""val"": 138, ""count"": 19}, {""val"": 140, ""count"": 8}, {""val"": 112, ""count"": 6}, {""val"": 110, ""count"": 6}, {""val"": 156, ""count"": 5}, {""val"": 150, ""count"": 5}, {""val"": 164, ""count"": 4}, {""val"": 134, ""count"": 4}, {""val"": 130, ""count"": 4}, {""val"": 148, ""count"": 4}, {""val"": 96, ""count"": 3}, {""val"": 84, ""count"": 3}, {""val"": 160, ""count"": 3}, {""val"": 152, ""count"": 3}, {""val"": 118, ""count"": 3}, {""val"": 144, ""count"": 3}, {""val"": 154, ""count"": 3}, {""val"": 64, ""count"": 2}, {""val"": 58, ""count"": 1}]","[{""val"": 1000.0, ""count"": 353}]",CC0,doi:10.18112/openneuro.ds006914.v1.0.3,
ds006921,[],,Ramne2025,38,152,2,Resting State,Clinical/Intervention,Other,16.961872800925924,69166826012.0,64.4 GB,openneuro,High Density Resting State EEG of Phantom Limb Pain and Controls,eeg,"[{""val"": 128, ""count"": 124}, {""val"": 64, ""count"": 28}]","[{""val"": 2400.0, ""count"": 152}]",CC0,doi:10.18112/openneuro.ds006921.v1.1.1,
ds006923,[],,Polo2025,140,280,1,Resting State,Clinical/Intervention,Other,37.333333333333336,8691122065.0,8.1 GB,openneuro,Dataset of Electroencephalograms of Juvenile Offenders,eeg,"[{""val"": 128, ""count"": 280}]","[{""val"": 128.0, ""count"": 280}]",CC0,doi:10.18112/openneuro.ds006923.v1.0.0,
ds006940,[],,Sarkar2025_StudyOF,7,935,15,Motor,Motor,Healthy,34.022077777777774,3883627381.0,3.6 GB,openneuro,Dataset: EEG-Controlled Exoskeleton for Walking and Standing - A Longitudinal Study of Healthy Individuals,eeg,"[{""val"": 64, ""count"": 935}]","[{""val"": 100.0, ""count"": 935}]",CC0,doi:10.18112/openneuro.ds006940.v1.0.0,
ds006945,[],,Sarkar2025_T1_Weighted_Structural,5,14,3,Visual,Motor,Healthy,2.110611111111111,5761229857.0,5.4 GB,openneuro,Dataset: T1-Weighted Structural MRI and fMRI of Participants Viewing Self-Avatar Exoskeleton Walking (11 SWS Cycles),eeg,"[{""val"": 64, ""count"": 14}]","[{""val"": 5000.0, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds006945.v1.2.1,
ds006963,[],,Ozdemir2025,32,32,1,Visual,Memory,Healthy,85.26408194444444,56696991879.0,52.8 GB,openneuro,Motor Control Processes Moderate Visual Working Memory Gating Dataset,eeg,"[{""val"": 64, ""count"": 32}]","[{""val"": 1000.0, ""count"": 32}]",CC0,doi:10.18112/openneuro.ds006963.v1.0.0,
ds006979,"[""Ramzaoui2024""]",author_year,Ramzaoui2025,53,56,3,Visual,Memory,Healthy,148.12,41389026706.0,38.5 GB,openneuro,Examining Perceptual Grouping on Stages of Processing in Visual Working Memory: An ERP Study,eeg,"[{""val"": 69, ""count"": 53}, {""val"": 72, ""count"": 1}]","[{""val"": 512.0, ""count"": 55}, {""val"": 500.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds006979.v1.0.1,
ds007006,[],,Wu2025,10,50,5,Multisensory,Affect,Healthy,3.6069444444444443,963375182.0,918.7 MB,openneuro,VR-Compassion Cultivation Training,eeg,"[{""val"": 64, ""count"": 50}]","[{""val"": 256.0, ""count"": 50}]",CC0,doi:10.18112/openneuro.ds007006.v1.0.0,
ds007020,[],,Jamshidi2025,94,94,1,Resting State,Clinical/Intervention,Parkinson's,4.106407222222223,1870056562.0,1.7 GB,openneuro,EEG Mortality Dataset in Parkinson's Disease,eeg,"[{""val"": 63, ""count"": 76}, {""val"": 64, ""count"": 18}]","[{""val"": 500.0, ""count"": 94}]",CC0,doi:10.18112/openneuro.ds007020.v1.0.0,
ds007028,"[""Kajikawa2000""]",author_year,Kajikawa2025,3,3,1,Auditory,Perception,Other,0.8074388888888889,14882843764.0,13.9 GB,openneuro,Auditory Cortex Macaque Monkey DISC Data,eeg,"[{""val"": 64, ""count"": 3}]","[{""val"": 20000.0, ""count"": 3}]",CC0,doi:10.18112/openneuro.ds007028.v1.0.0,
ds007052,"[""Couperus2021_N400""]",author_year,Couperus2025_N400,288,288,1,Visual,Memory,Healthy,40.00969111111111,9636979355.0,9.0 GB,openneuro,PURSUE N400 Word Processing,eeg,"[{""val"": 32, ""count"": 288}]","[{""val"": 500.0, ""count"": 288}]",CC0,doi:10.18112/openneuro.ds007052.v1.1.2,
ds007056,"[""Couperus2021_P300""]",author_year,Couperus2025_P300,286,286,1,Visual,Attention,Healthy,34.86793055555555,8413923956.0,7.8 GB,openneuro,PURSUE P300 Visual Oddball,eeg,"[{""val"": 32, ""count"": 286}]","[{""val"": 500.0, ""count"": 286}]",CC0,doi:10.18112/openneuro.ds007056.v1.1.1,
ds007069,"[""Couperus2021_MMN""]",author_year,Couperus2025_MMN,281,281,1,Auditory,Perception,Healthy,54.90152944444444,13336113822.0,12.4 GB,openneuro,PURSUE MMN Auditory Oddball,eeg,"[{""val"": 32, ""count"": 281}]","[{""val"": 500.0, ""count"": 281}]",CC0,doi:10.18112/openneuro.ds007069.v1.0.0,
ds007081,[],,Ylmaz2025,41,41,1,Visual,Memory,Healthy,26.319111111111113,12129532986.0,11.3 GB,openneuro,"Passive but accessible:  Studied information is not actively stored in working memory, yet attended regardless of anticipated load",eeg,"[{""val"": 32, ""count"": 41}]","[{""val"": 1000.0, ""count"": 41}]",CC0,doi:10.18112/openneuro.ds007081.v1.0.0,
ds007095,[],,Feng2025,8,6019,1,Other,Clinical/Intervention,Epilepsy,154.5963888888889,521941815.0,497.8 MB,openneuro,RNS_Epilepsy-iBIDS,ieeg,"[{""val"": 2, ""count"": 6019}]","[{""val"": 200.0, ""count"": 6019}]",CC0,doi:10.18112/openneuro.ds007095.v1.0.0,
ds007096,"[""Couperus2017""]",author_year,Couperus2025_PURSUE_N170_Face,292,292,1,Visual,Perception,Healthy,51.75700166666667,12503279073.0,11.6 GB,openneuro,PURSUE N170 Face Perception,eeg,"[{""val"": 32, ""count"": 292}]","[{""val"": 500.0, ""count"": 292}]",CC0,doi:10.18112/openneuro.ds007096.v1.0.0,
ds007118,"[""Hatano""]",canonical,Hatano2025_part1,65,82,1,Sleep,Sleep,Unknown,44.215,36317646610.0,33.8 GB,openneuro,iEEG_comprehensive_HFA_model_part1,ieeg,"[{""val"": 128, ""count"": 21}, {""val"": 112, ""count"": 17}, {""val"": 124, ""count"": 6}, {""val"": 102, ""count"": 5}, {""val"": 108, ""count"": 4}, {""val"": 120, ""count"": 4}, {""val"": 68, ""count"": 3}, {""val"": 116, ""count"": 3}, {""val"": 138, ""count"": 3}, {""val"": 118, ""count"": 3}, {""val"": 106, ""count"": 2}, {""val"": 144, ""count"": 2}, {""val"": 64, ""count"": 2}, {""val"": 122, ""count"": 1}, {""val"": 114, ""count"": 1}, {""val"": 74, ""count"": 1}, {""val"": 94, ""count"": 1}, {""val"": 36, ""count"": 1}, {""val"": 132, ""count"": 1}, {""val"": 58, ""count"": 1}]","[{""val"": 1000.0, ""count"": 82}]",CC0,doi:10.18112/openneuro.ds007118.v1.0.0,
ds007119,[],author_year,Hatano2025_part3,103,106,1,Sleep,Sleep,Unknown,41.775,34981740938.0,32.6 GB,openneuro,iEEG_comprehensive_HFA_model_part3,ieeg,"[{""val"": 128, ""count"": 12}, {""val"": 124, ""count"": 5}, {""val"": 86, ""count"": 4}, {""val"": 58, ""count"": 4}, {""val"": 134, ""count"": 4}, {""val"": 120, ""count"": 4}, {""val"": 102, ""count"": 4}, {""val"": 78, ""count"": 4}, {""val"": 94, ""count"": 4}, {""val"": 100, ""count"": 3}, {""val"": 110, ""count"": 3}, {""val"": 118, ""count"": 3}, {""val"": 136, ""count"": 2}, {""val"": 112, ""count"": 2}, {""val"": 96, ""count"": 2}, {""val"": 130, ""count"": 2}, {""val"": 132, ""count"": 2}, {""val"": 148, ""count"": 2}, {""val"": 64, ""count"": 2}, {""val"": 108, ""count"": 2}, {""val"": 74, ""count"": 2}, {""val"": 84, ""count"": 2}, {""val"": 34, ""count"": 2}, {""val"": 72, ""count"": 2}, {""val"": 140, ""count"": 2}, {""val"": 122, ""count"": 1}, {""val"": 126, ""count"": 1}, {""val"": 52, ""count"": 1}, {""val"": 6, ""count"": 1}, {""val"": 116, ""count"": 1}, {""val"": 73, ""count"": 1}, {""val"": 114, ""count"": 1}, {""val"": 90, ""count"": 1}, {""val"": 76, ""count"": 1}, {""val"": 70, ""count"": 1}, {""val"": 48, ""count"": 1}, {""val"": 88, ""count"": 1}, {""val"": 54, ""count"": 1}, {""val"": 146, ""count"": 1}, {""val"": 180, ""count"": 1}, {""val"": 135, ""count"": 1}, {""val"": 138, ""count"": 1}, {""val"": 142, ""count"": 1}, {""val"": 28, ""count"": 1}, {""val"": 152, ""count"": 1}, {""val"": 82, ""count"": 1}, {""val"": 46, ""count"": 1}, {""val"": 38, ""count"": 1}, {""val"": 144, ""count"": 1}, {""val"": 44, ""count"": 1}, {""val"": 104, ""count"": 1}]","[{""val"": 1000.0, ""count"": 106}]",CC0,doi:10.18112/openneuro.ds007119.v1.0.0,
ds007120,[],author_year,Hatano2025_part2,65,70,1,Sleep,Sleep,Epilepsy,41.863055555555555,35448518496.0,33.0 GB,openneuro,iEEG_comprehensive_HFA_model_part2,ieeg,"[{""val"": 128, ""count"": 13}, {""val"": 112, ""count"": 10}, {""val"": 104, ""count"": 5}, {""val"": 132, ""count"": 4}, {""val"": 110, ""count"": 4}, {""val"": 118, ""count"": 3}, {""val"": 138, ""count"": 2}, {""val"": 150, ""count"": 2}, {""val"": 56, ""count"": 2}, {""val"": 126, ""count"": 2}, {""val"": 120, ""count"": 2}, {""val"": 108, ""count"": 2}, {""val"": 130, ""count"": 2}, {""val"": 140, ""count"": 2}, {""val"": 106, ""count"": 2}, {""val"": 100, ""count"": 2}, {""val"": 34, ""count"": 1}, {""val"": 144, ""count"": 1}, {""val"": 156, ""count"": 1}, {""val"": 116, ""count"": 1}, {""val"": 122, ""count"": 1}, {""val"": 136, ""count"": 1}, {""val"": 84, ""count"": 1}, {""val"": 134, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 98, ""count"": 1}, {""val"": 164, ""count"": 1}]","[{""val"": 1000.0, ""count"": 70}]",CC0,doi:10.18112/openneuro.ds007120.v1.0.0,
ds007137,"[""Couperus2021_N2pc""]",author_year,Couperus2025_N2PC,294,294,1,Visual,Attention,Healthy,54.44088055555556,13143663368.0,12.2 GB,openneuro,PURSUE N2pc Visual Search,eeg,"[{""val"": 32, ""count"": 294}]","[{""val"": 500.0, ""count"": 294}]",CC0,doi:10.18112/openneuro.ds007137.v1.0.0,
ds007139,"[""Couperus2021_LRP""]",author_year,Couperus2025_LRP,292,292,1,Visual,Attention,Healthy,64.58555888888888,15598996691.0,14.5 GB,openneuro,PURSUE LRP/ERN Flanker,eeg,"[{""val"": 32, ""count"": 292}]","[{""val"": 500.0, ""count"": 292}]",CC0,doi:10.18112/openneuro.ds007139.v1.0.0,
ds007162,[],author_year,DS7162_VisualRecognition,34,69,1,Visual,Perception,Healthy,71.81638055555555,65339062772.0,60.9 GB,openneuro,Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG),eeg,"[{""val"": 63, ""count"": 69}]","[{""val"": 1000.0, ""count"": 69}]",CC0,doi:10.18112/openneuro.ds007162.v1.0.0,
ds007169,"[""Barras2021""]",author_year,Barras2026_Multimodal,18,18,1,Visual,Memory,Healthy,5.090633333333333,442139020.0,421.7 MB,openneuro,"Multimodal Cognitive Workload n-back Task, 4 Difficulties",eeg,"[{""val"": 24, ""count"": 18}]","[{""val"": 250.0, ""count"": 18}]",CC0,doi:10.18112/openneuro.ds007169.v1.0.5,
ds007172,"[""EEGAsymmetries""]",canonical,Reinke2026,100,501,6,Visual,Attention,Healthy,50.98400083333333,11852106397.0,11.0 GB,openneuro,EEG-Asymmetries Dataset,eeg,"[{""val"": 32, ""count"": 496}, {""val"": 29, ""count"": 5}]","[{""val"": 500.0, ""count"": 496}, {""val"": 1000.0, ""count"": 5}]",CC0,doi:10.18112/openneuro.ds007172.v1.0.0,
ds007175,[],canonical,DS7175_FFR_ActiveListening,41,41,1,Auditory,Perception,Healthy,46.89787261111111,215211348736.0,200.4 GB,openneuro,FFR-active-listening,eeg,"[{""val"": 65, ""count"": 41}]","[{""val"": 5000.0, ""count"": 41}]",CC0,doi:10.18112/openneuro.ds007175.v1.0.1,
ds007176,[],,Isaza2026_Longitudinal,45,300,2,Resting State,Resting-state,Healthy,26.174016666666667,22616298800.0,21.1 GB,openneuro,Longitudinal EEG Test-Retest Reliability in Healthy Individuals,eeg,"[{""val"": 60, ""count"": 300}]","[{""val"": 1000.0, ""count"": 300}]",CC0,doi:10.18112/openneuro.ds007176.v1.0.1,
ds007180,"[""FuentesGuerra2024""]",author_year,FuentesGuerra2026,25,25,1,Unknown,Unknown,Healthy,34.725588888888886,15755538005.0,14.7 GB,openneuro,Exo-EEG Experiment,eeg,"[{""val"": 63, ""count"": 25}]","[{""val"": 500.0, ""count"": 25}]",CC0,doi:10.18112/openneuro.ds007180.v1.0.0,
ds007181,[],,Li2026,59,59,1,Sleep,Clinical/Intervention,Other,454.4836111111111,63514978596.0,59.2 GB,openneuro,"Structural MRI, Resting-state fMRI, and PSG/EEG Dataset of Zoster-associated Neuralgia",eeg,"[{""val"": 24, ""count"": 59}]","[{""val"": 1024.0, ""count"": 59}]",CC0,doi:10.18112/openneuro.ds007181.v1.0.1,
ds007216,"[""Kucyi2024""]",author_year,Kucyi2026,24,187,2,Visual,Attention,Healthy,33.29890561111111,112422123313.0,104.7 GB,openneuro,A multi-session simultaneous EEG-fMRI dataset with online experience sampling,eeg,"[{""val"": 36, ""count"": 186}]","[{""val"": 5000.0, ""count"": 186}]",CC0,doi:10.18112/openneuro.ds007216.v1.0.0,
ds007221,[],,Xinwei2026,84,1265,4,Visual,Motor,Healthy,135.3057763888889,133984759249.0,124.8 GB,openneuro,Cross-Environment Multi-Paradigm Motor Imagery EEG Dataset,eeg,"[{""val"": 69, ""count"": 1023}, {""val"": 68, ""count"": 220}, {""val"": 64, ""count"": 22}]","[{""val"": 1000.0, ""count"": 1265}]",CC0,doi:10.18112/openneuro.ds007221.v1.0.1,
ds007262,"[""Barras2025""]",author_year,Barras2026_Cognitive,18,18,1,Unknown,Attention,Healthy,4.58342,397274375.0,378.9 MB,openneuro,Cognitive Workload 8-level arithmetic,eeg,"[{""val"": 24, ""count"": 18}]","[{""val"": 250.0, ""count"": 18}]",CC0,doi:10.18112/openneuro.ds007262.v1.0.6,
ds007314,"[""Martzoukou2024_Post""]",author_year,Martzoukou2026_tACS,2,14,1,Visual,Clinical/Intervention,Other,4.888012222222223,1165891095.0,1.1 GB,openneuro,tACS for Patients with Post-Stroke Anomia,eeg,"[{""val"": 32, ""count"": 14}]","[{""val"": 500.0, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds007314.v1.0.0,
ds007315,"[""Martzoukou2024_Post_A""]",author_year,Martzoukou2026_tACS_Patients,2,14,1,Visual,Clinical/Intervention,Other,4.888012222222223,1165892937.0,1.1 GB,openneuro,tACS for Patients with Post-Stroke Anomia,eeg,"[{""val"": 32, ""count"": 14}]","[{""val"": 500.0, ""count"": 14}]",CC0,doi:10.18112/openneuro.ds007315.v1.0.1,
ds007322,"[""Mishra2024""]",author_year,Mishra2026,57,57,1,Auditory,Attention,Healthy,48.701123055555556,45648502420.0,42.5 GB,openneuro,Personalized smartphone notifications bias auditory salience across processing stages,eeg,"[{""val"": 64, ""count"": 31}, {""val"": 66, ""count"": 26}]","[{""val"": 1000.0, ""count"": 57}]",CC0,doi:10.18112/openneuro.ds007322.v1.0.1,
ds007338,"[""EEGEyeNet_v2"", ""EEGEYENET""]",canonical,Plomecka2026,1,1,1,Visual,Perception,Healthy,0.0898511111111111,41876427.0,39.9 MB,openneuro,EEGEyeNet Dataset,eeg,"[{""val"": 129, ""count"": 1}]","[{""val"": 500.0, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds007338.v1.0.0,
ds007347,[],,Elias2026,5,10,1,Resting State,Clinical/Intervention,Cancer,4.473611111111111,1767378590.0,1.6 GB,openneuro,Sterotactic Focused Ultrasound Mesencephalotomy for the Treatment of Head and Neck Cancer Pain,eeg,"[{""val"": 50, ""count"": 6}, {""val"": 102, ""count"": 4}]","[{""val"": 256.0, ""count"": 6}, {""val"": 512.0, ""count"": 4}]",CC0,doi:10.18112/openneuro.ds007347.v1.0.0,
ds007353,"[""HAD_MEEG"", ""HADMEEG""]",canonical,Zhang2026,32,473,2,Visual,Perception,Healthy,44.82657291666667,193902192538.0,180.6 GB,openneuro,HAD-MEEG,"eeg, meg","[{""val"": 409, ""count"": 240}, {""val"": 64, ""count"": 224}, {""val"": 378, ""count"": 9}]","[{""val"": 1200.0, ""count"": 249}, {""val"": 1000.0, ""count"": 224}]",CC0,doi:10.18112/openneuro.ds007353.v1.0.0,
ds007358,"[""Vianney2025""]",author_year,Vianney2026,2000,6000,3,Resting State,Resting-state,Healthy,276.1350466579861,17264324344.0,16.1 GB,openneuro,A subset of large-scale EEG dataset (India + Tanzania),eeg,"[{""val"": 62, ""count"": 2408}, {""val"": 60, ""count"": 833}, {""val"": 74, ""count"": 811}, {""val"": 72, ""count"": 770}, {""val"": 68, ""count"": 707}, {""val"": 50, ""count"": 216}, {""val"": 66, ""count"": 150}, {""val"": 56, ""count"": 63}, {""val"": 48, ""count"": 29}, {""val"": 44, ""count"": 6}, {""val"": 54, ""count"": 4}, {""val"": 65, ""count"": 3}]","[{""val"": 128.0, ""count"": 5733}, {""val"": 256.0, ""count"": 267}]",CC0,doi:10.18112/openneuro.ds007358.v1.0.0,
ds007406,"[""Edit2024""]",author_year,Edit2026,10,10,1,Multisensory,Affect,Healthy,0.5000651041666667,27012236.0,25.8 MB,openneuro,EEG dataset on consumer responses to extreme versus traditional marketing videos,eeg,"[{""val"": 14, ""count"": 10}]","[{""val"": 256.0, ""count"": 10}]",CC0,doi:10.18112/openneuro.ds007406.v1.0.0,
ds007420,"[""Gao2024""]",author_year,Gao2026_Light_Weight_Multi,12,60,4,Motor,Motor,Healthy,,587945590.0,560.7 MB,openneuro,A Light Weight Multi-Distance fNIRS Dataset for Ball-Squeezing Task and Purposeful Motion Artifact Creation Task,fnirs,"[{""val"": 200, ""count"": 60}]","[{""val"": 8.719308035714286, ""count"": 52}, {""val"": 11.625744047619047, ""count"": 4}, {""val"": 8.719308035714288, ""count"": 3}, {""val"": 11.625744047619051, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds007420.v1.0.2,
ds007427,"[""HenaoIsaza2026""]",author_year,Isaza2026_Comprehensive,44,44,1,Resting State,Clinical/Intervention,Dementia,3.9115433333333334,3379868887.0,3.1 GB,openneuro,Comprehensive methodology for sample enrichment in EEG biomarker studies for Alzheimer’s risk classification,eeg,"[{""val"": 60, ""count"": 44}]","[{""val"": 1000.0, ""count"": 44}]",CC0,doi:10.18112/openneuro.ds007427.v1.0.1,
ds007431,"[""Ataseven2024""]",author_year,Ataseven2026,47,47,1,Visual,Memory,Healthy,160.1695947222222,155226967884.0,144.6 GB,openneuro,Diffuse predictions stabilize and reshape neural code during memory encoding,eeg,"[{""val"": 66, ""count"": 47}]","[{""val"": 1000.0, ""count"": 47}]",CC0,doi:10.18112/openneuro.ds007431.v1.0.0,
ds007445,[],author_year,Panchavati2026,19,66,1,Other,Clinical/Intervention,Epilepsy,73.70927875000001,54201403733.0,50.5 GB,openneuro,Thalamocortical ictal iEEG dataset,ieeg,"[{""val"": 140, ""count"": 10}, {""val"": 138, ""count"": 10}, {""val"": 83, ""count"": 6}, {""val"": 265, ""count"": 6}, {""val"": 202, ""count"": 5}, {""val"": 216, ""count"": 5}, {""val"": 162, ""count"": 4}, {""val"": 203, ""count"": 3}, {""val"": 112, ""count"": 3}, {""val"": 68, ""count"": 2}, {""val"": 49, ""count"": 2}, {""val"": 81, ""count"": 2}, {""val"": 263, ""count"": 1}, {""val"": 120, ""count"": 1}, {""val"": 201, ""count"": 1}, {""val"": 139, ""count"": 1}, {""val"": 111, ""count"": 1}, {""val"": 124, ""count"": 1}, {""val"": 261, ""count"": 1}, {""val"": 137, ""count"": 1}]","[{""val"": 200.0, ""count"": 42}, {""val"": 2000.0, ""count"": 17}, {""val"": 200.00000000000003, ""count"": 6}, {""val"": 1999.9999999999998, ""count"": 1}]",CC0,doi:10.18112/openneuro.ds007445.v1.0.2,
ds007454,[],author_year,DS7454_TimePerception,42,42,1,Visual,Perception,Healthy,37.15316055555555,31812516010.0,29.6 GB,openneuro,A common neural mechanism underlies experiences of passage of time,eeg,"[{""val"": 64, ""count"": 42}]","[{""val"": 1000.0, ""count"": 42}]",CC0,doi:10.18112/openneuro.ds007454.v1.0.1,
ds007463,"[""Fogarty2025""]",author_year,Fogarty2026_Very,8,88,14,Visual,Perception,Healthy,18.898382222222224,74413175764.0,69.3 GB,openneuro,Very-High-Density Diffuse Optical Tomography System Validation Dataset,fnirs,"[{""val"": 19086, ""count"": 14}, {""val"": 19426, ""count"": 11}, {""val"": 21518, ""count"": 11}, {""val"": 19620, ""count"": 11}, {""val"": 19528, ""count"": 11}, {""val"": 19908, ""count"": 10}, {""val"": 20218, ""count"": 10}, {""val"": 20874, ""count"": 10}]","[{""val"": 7.8125, ""count"": 88}]",CC0,doi:10.18112/openneuro.ds007463.v1.1.1,
ds007471,"[""Zhou2024""]",author_year,Zhou2026,31,31,1,Auditory,Other,Healthy,18.782294444444442,8658914750.0,8.1 GB,openneuro,Joint agency EEG dataset,eeg,"[{""val"": 64, ""count"": 31}]","[{""val"": 1000.0, ""count"": 31}]",CC0,doi:10.18112/openneuro.ds007471.v1.0.0,
ds007473,"[""Tripathy2024""]",author_year,Fogarty2026_High,5,189,19,Multisensory,Perception,Healthy,51.12245333333332,38961072704.0,36.3 GB,openneuro,High-Density Diffuse Optical Tomography Audiovisual Movie Viewing Dataset,fnirs,"[{""val"": 6782, ""count"": 71}, {""val"": 6928, ""count"": 43}, {""val"": 6880, ""count"": 38}, {""val"": 6750, ""count"": 31}, {""val"": 7030, ""count"": 6}]","[{""val"": 10.41666666666667, ""count"": 189}]",CC0,doi:10.18112/openneuro.ds007473.v1.0.0,
ds007477,[],,Niu2026,18,36,1,Other,Unknown,Unknown,,9384.0,9.2 KB,openneuro,TimeSeries BIDS converted,fnirs,"[{""val"": 1, ""count"": 36}]","[{""val"": 10.0, ""count"": 36}]",CC0,doi:10.18112/openneuro.ds007477.v1.0.1,
ds007521,"[""Moerel2025""]",author_year,Moerel2026,23,46,1,Visual,Attention,Healthy,34.23017222222222,31115320529.0,29.0 GB,openneuro,The effect of hunger and state preferences on the neural processing of food images,eeg,"[{""val"": 64, ""count"": 46}]","[{""val"": 100.0, ""count"": 46}]",CC0,doi:10.18112/openneuro.ds007521.v1.0.1,
ds007523,"[""Dascoli2025""]",author_year,Bel2026,58,579,1,Auditory,Perception,Healthy,94.80763305555556,477619624392.0,444.8 GB,openneuro,LPP MEG Listen,meg,"[{""val"": 346, ""count"": 484}, {""val"": 404, ""count"": 9}, {""val"": 400, ""count"": 9}, {""val"": 329, ""count"": 9}, {""val"": 343, ""count"": 9}, {""val"": 321, ""count"": 1}]","[{""val"": 1000.0, ""count"": 521}]",CC0,doi:10.18112/openneuro.ds007523.v1.0.0,
ds007524,"[""LittlePrince""]",canonical,Pallier2025,50,500,1,Visual,Other,Healthy,63.99154166666667,320573795930.0,298.6 GB,openneuro,LittlePrince_MEG_French_Read_Pallier2025,meg,"[{""val"": 346, ""count"": 414}, {""val"": 339, ""count"": 27}, {""val"": 338, ""count"": 9}]","[{""val"": 1000.0, ""count"": 450}]",CC0,doi:10.18112/openneuro.ds007524.v1.0.1,
ds007526,"[""PD_EEG"", ""PDEEG""]",canonical,Katzir2026,144,277,2,Motor,Clinical/Intervention,Parkinson's,19.488375555555557,4578749719.0,4.3 GB,openneuro,PD-EEG: Resting-State & Walking EEG in Parkinson's Disease,eeg,"[{""val"": 65, ""count"": 277}]","[{""val"": 250.0, ""count"": 277}]",CC0,doi:10.18112/openneuro.ds007526.v1.0.0,
ds007554,[],,Ajra2026,30,1034,7,Unknown,Other,Healthy,61.884018024889436,4515882153.0,4.2 GB,openneuro,Multimodal dataset from the CMx7-MM Experiment,"eeg, fnirs","[{""val"": 32, ""count"": 1034}]","[{""val"": 10.0, ""count"": 519}, {""val"": 250.002243692634, ""count"": 26}, {""val"": 250.00227279699027, ""count"": 13}, {""val"": 250.00027916425347, ""count"": 12}, {""val"": 250.00220003611236, ""count"": 12}, {""val"": 250.0003082681523, ""count"": 10}, {""val"": 250.00206906663882, ""count"": 7}, {""val"": 250.0003344161922, ""count"": 7}, {""val"": 250.0022582448113, ""count"": 7}, {""val"": 249.99969708769964, ""count"": 7}, {""val"": 250.00079612894984, ""count"": 7}, {""val"": 250.0003892134066, ""count"": 7}, {""val"": 250.0009023984434, ""count"": 7}, {""val"": 249.9997989509009, ""count"": 7}, {""val"": 250.0001952063645, ""count"": 7}, {""val"": 250.0004683397171, ""count"": 7}, {""val"": 250.0001184960745, ""count"": 7}, {""val"": 250.00098976743945, ""count"": 7}, {""val"": 250.00004633330667, ""count"": 7}, {""val"": 250.00197788831903, ""count"": 7}, {""val"": 250.00085794657687, ""count"": 7}, {""val"": 250.00092087268928, ""count"": 7}, {""val"": 250.0006918490773, ""count"": 7}, {""val"": 250.00020640453602, ""count"": 7}, {""val"": 250.0009635339967, ""count"": 7}, {""val"": 249.99990081418517, ""count"": 7}, {""val"": 249.99995902181342, ""count"": 7}, {""val"": 250.00038784916032, ""count"": 7}, {""val"": 250.00028848659534, ""count"": 7}, {""val"": 250.0007861528667, ""count"": 7}, {""val"": 250.0003228201043, ""count"": 7}, {""val"": 249.99991536608968, ""count"": 7}, {""val"": 250.00119525819466, ""count"": 7}, {""val"": 250.00208361879578, ""count"": 7}, {""val"": 249.99972619146294, ""count"": 7}, {""val"": 250.00217093177304, ""count"": 7}, {""val"": 250.00094787351506, ""count"": 6}, {""val"": 250.0020981709544, ""count"": 6}, {""val"": 249.99996084080226, ""count"": 6}, {""val"": 250.00055565156606, ""count"": 6}, {""val"": 250.0004374167852, ""count"": 6}, {""val"": 250.00064410035029, ""count"": 6}, {""val"": 250.00228734917093, ""count"": 6}, {""val"": 250.00205451448358, ""count"": 6}, {""val"": 250.00056292755625, ""count"": 6}, {""val"": 250.0006486478473, ""count"": 6}, {""val"": 250.00001813896364, ""count"": 6}, {""val"": 250.00026461230658, ""count"": 6}, {""val"": 250.00067934345654, ""count"": 6}, {""val"": 250.0002209564761, ""count"": 6}, {""val"": 250.00218548394184, ""count"": 6}, {""val"": 250.00074721488596, ""count"": 6}, {""val"": 250.00023550841792, ""count"": 6}, {""val"": 250.00157429431061, ""count"": 6}, {""val"": 250.0001383628698, ""count"": 6}, {""val"": 250.00057674056998, ""count"": 6}, {""val"": 250.0009258749471, ""count"": 6}, {""val"": 250.00077165770458, ""count"": 6}, {""val"": 250.00231645353733, ""count"": 6}, {""val"": 250.0006891205781, ""count"": 6}, {""val"": 250.00080547975216, ""count"": 6}, {""val"": 250.0004249680335, ""count"": 6}, {""val"": 250.00278212432153, ""count"": 6}, {""val"": 250.00195992551573, ""count"": 6}, {""val"": 250.0008785240374, ""count"": 5}, {""val"": 250.00310227399197, ""count"": 5}, {""val"": 250.00043923578096, ""count"": 5}, {""val"": 250.00045378774817, ""count"": 5}, {""val"": 250.00042468381545, ""count"": 5}, {""val"": 250.00072163519718, ""count"": 5}, {""val"": 250.00041013185162, ""count"": 5}, {""val"": 249.99993719394965, ""count"": 4}, {""val"": 250.00224414738952, ""count"": 4}, {""val"": 250.0004295723662, ""count"": 3}, {""val"": 250.00074846544865, ""count"": 3}, {""val"": 249.99984988253266, ""count"": 3}, {""val"": 249.99994446990382, ""count"": 3}, {""val"": 250.0005021616826, ""count"": 3}, {""val"": 250.00218593869715, ""count"": 2}, {""val"": 250.00059930751343, ""count"": 2}, {""val"": 250.00041146767637, ""count"": 2}]",CC0,doi:10.18112/openneuro.ds007554.v1.0.0,
ds007558,[],,Qi2026,67,121,1,Resting State,Clinical/Intervention,Unknown,25.984918055555557,719738454.0,686.4 MB,openneuro,EEG Pre/Post Intervention Dataset,eeg,"[{""val"": 19, ""count"": 106}, {""val"": 21, ""count"": 13}, {""val"": 20, ""count"": 2}]","[{""val"": 200.0, ""count"": 121}]",CC0,doi:10.18112/openneuro.ds007558.v1.0.0,
ds007591,"[""Sato2025""]",author_year,Sato2026_Delineating,3,21,3,Unknown,Motor,Healthy,6.775833333333333,1737849881.0,1.6 GB,openneuro,Delineating neural contributions to EEG-based speech decoding,eeg,"[{""val"": 139, ""count"": 21}]","[{""val"": 256.0, ""count"": 21}]",CC0,doi:10.18112/openneuro.ds007591.v1.0.1,
ds007602,"[""Sato2024""]",author_year,Sato2026_Speech,3,113,1,Visual,Motor,Healthy,44.18638888888889,53309491557.0,49.6 GB,openneuro,EEG-Speech Brain Decoding Dataset,eeg,"[{""val"": 134, ""count"": 113}]","[{""val"": 1200.0, ""count"": 113}]",CC0,doi:10.18112/openneuro.ds007602.v1.0.1,
ds007609,"[""Shalamberidze2025""]",author_year,Shalamberidze2026,51,51,1,Resting State,Affect,Healthy,4.057284444444445,7528851369.0,7.0 GB,openneuro,Resting-State EEG and Trait Anxiety,eeg,"[{""val"": 256, ""count"": 51}]","[{""val"": 500.0, ""count"": 51}]",CC0,doi:10.18112/openneuro.ds007609.v1.0.0,
ds007615,"[""Normannseth2026""]",author_year,Normannseth2026,69,192,2,Auditory,Perception,Healthy,18.548532307942708,37200493446.0,34.6 GB,openneuro,LDAEP and resting-state EEG in healthy women,eeg,"[{""val"": 68, ""count"": 192}]","[{""val"": 2048.0, ""count"": 192}]",CC0,doi:10.18112/openneuro.ds007615.v1.0.0,
nm000103,"[""HealthyBrainNetwork"", ""HBN_EEG_NC"", ""HBN_NoCommercial""]",canonical,Shirazi2017,447,3522,10,,,,285.01504277777775,268707776275.0,250.3 GB,nemar,Healthy Brain Network EEG - Not for Commercial Use,eeg,"[{""val"": 129, ""count"": 3522}]","[{""val"": 500, ""count"": 3522}]",CC-BY-NC-SA 4.0,10.82901/nemar.nm000103,
nm000104,"[""emg2qwerty""]",canonical,Sivakumar2024,108,1136,1,,,,346.3244476388889,239744967765.0,223.3 GB,nemar,emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography,emg,"[{""val"": 32, ""count"": 1136}]","[{""val"": 2000, ""count"": 1136}]",CC-BY-NC-SA-4.0,10.82901/nemar.nm000104,
nm000105,"[""FRL_DiscreteGestures""]",canonical,Kaifosh2025,100,100,1,,,,63.937591805555556,22108979946.0,20.6 GB,nemar,FRL Discrete Gestures: Hand Gesture Recognition from Surface Electromyography,emg,"[{""val"": 16, ""count"": 100}]","[{""val"": 2000, ""count"": 100}]",CC-BY-NC 4.0,10.82901/nemar.nm000105,
nm000106,"[""FRL_Handwriting""]",canonical,Kaifosh2025_106,100,807,1,,,,140.70160930555554,48680269795.0,45.3 GB,nemar,FRL Handwriting: Handwriting Decoding from Surface Electromyography,emg,"[{""val"": 16, ""count"": 807}]","[{""val"": 2000, ""count"": 807}]",CC-BY-NC 4.0,10.82901/nemar.nm000106,
nm000107,"[""FRL_WristControl""]",canonical,Kaifosh2025_107,100,182,1,,,,77.18355888888888,26685154019.0,24.9 GB,nemar,FRL Wrist Control: Wrist Movement Decoding from Surface Electromyography,emg,"[{""val"": 16, ""count"": 182}]","[{""val"": 2000, ""count"": 182}]",CC-BY-NC 4.0,10.82901/nemar.nm000107,
nm000108,"[""HySER"", ""Hyser""]",canonical,Jiang2021,20,1514,38,,,,,116163622979.0,108.2 GB,nemar,HySER: High-Density Surface Electromyogram Recordings,emg,"[{""val"": 256, ""count"": 1514}]",[],ODC-By-1.0,10.82901/nemar.nm000108,
nm000109,[],canonical,Zyma2019,36,72,2,,,,2.40996,182988920.0,174.5 MB,nemar,EEG During Mental Arithmetic Tasks,eeg,"[{""val"": 21, ""count"": 72}]","[{""val"": 500, ""count"": 72}]",ODC-By-1.0,10.82901/nemar.nm000109,
nm000110,"[""CHBMIT"", ""CHB_MIT""]",canonical,Connolly2010,24,686,1,,,,982.9345334201389,45759387214.0,42.6 GB,nemar,CHB-MIT,eeg,"[{""val"": 23, ""count"": 306}, {""val"": 28, ""count"": 259}, {""val"": 38, ""count"": 39}, {""val"": 22, ""count"": 36}, {""val"": 24, ""count"": 30}, {""val"": 29, ""count"": 14}, {""val"": 25, ""count"": 1}, {""val"": 31, ""count"": 1}]","[{""val"": 256, ""count"": 686}]",ODC-By-1.0,10.82901/nemar.nm000110,
nm000112,"[""FACED""]",canonical,Liu2024_112,123,123,1,,,,155.28158666666664,33722263208.0,31.4 GB,nemar,FACED - Finer-grained Affective Computing EEG Dataset,eeg,"[{""val"": 32, ""count"": 123}]","[{""val"": 1000, ""count"": 68}, {""val"": 250, ""count"": 55}]",CC-BY-4.0,10.82901/nemar.nm000112,
nm000113,[],canonical,Lee2020,15,45,1,,,,5.175732421875,613632820.0,585.2 MB,nemar,"2020 BCI competition, track 3",eeg,"[{""val"": 64, ""count"": 45}]","[{""val"": 256, ""count"": 45}]",CC-BY-4.0,10.82901/nemar.nm000113,
nm000114,[],canonical,Mumtaz2017,64,181,3,,,,20.55563693576389,852233444.0,812.8 MB,nemar,MDD Patients and Healthy Controls EEG Data,eeg,"[{""val"": 22, ""count"": 112}, {""val"": 20, ""count"": 69}]","[{""val"": 256, ""count"": 181}]",CC-BY-4.0,10.82901/nemar.nm000114,
nm000115,[],canonical,Zhou2016,4,24,1,,,,6.268284444444444,159444069.0,152.1 MB,nemar,Zhou2016,eeg,"[{""val"": 14, ""count"": 24}]","[{""val"": 250, ""count"": 24}]",CC-BY-4.0,10.82901/nemar.nm000115,
nm000118,[],canonical,Nakanishi2015,9,9,1,Visual,Perception,Healthy,2.133974609375,68545404.0,65.4 MB,nemar,Nakanishi2015 – SSVEP Nakanishi 2015 dataset,eeg,"[{""val"": 8, ""count"": 9}]","[{""val"": 256.0, ""count"": 9}]",Unknown,,
nm000119,"[""Oikonomou2016""]",author_year,Oikonomou2016_MAMEM1,11,47,1,Visual,Perception,Healthy,6.22372,5751425358.0,5.4 GB,nemar,Oikonomou2016 – SSVEP MAMEM 1 dataset,eeg,"[{""val"": 256, ""count"": 47}]","[{""val"": 250.0, ""count"": 47}]",ODC-By-1.0,,
nm000120,"[""MAMEM2"", ""SSVEPMAMEM2"", ""MAMEM2_SSVEP""]",canonical,Oikonomou2016_MAMEM2,11,55,1,Visual,Attention,Healthy,5.1091766666666665,4726025611.0,4.4 GB,nemar,Oikonomou2016 – SSVEP MAMEM 2 dataset,eeg,"[{""val"": 256, ""count"": 55}]","[{""val"": 250.0, ""count"": 55}]",ODC-By-1.0,,
nm000121,"[""MAMEM3"", ""SSVEP_MAMEM3""]",canonical,Oikonomou2016_MAMEM3,11,110,1,Visual,Perception,Healthy,4.597261284722222,126061931.0,120.2 MB,nemar,Oikonomou2016 – SSVEP MAMEM 3 dataset,eeg,"[{""val"": 14, ""count"": 110}]","[{""val"": 128.0, ""count"": 110}]",ODC-By-1.0,,
nm000122,[],author_year,Chen2017,12,12,1,Visual,Perception,Healthy,3.2708430989583333,777922244.0,741.9 MB,nemar,Chen2017 – Single-flicker online SSVEP BCI dataset,eeg,"[{""val"": 32, ""count"": 12}]","[{""val"": 512.0, ""count"": 12}]",CC BY 4.0,,
nm000123,[],author_year,Kalunga2016,12,30,1,Visual,Perception,Healthy,2.589654947916667,82000354.0,78.2 MB,nemar,Kalunga2016 – SSVEP Exo dataset,eeg,"[{""val"": 8, ""count"": 30}]","[{""val"": 256.0, ""count"": 30}]",CC-BY-4.0,,
nm000124,[],author_year,Han2024,24,48,1,Visual,Perception,Healthy,19.839986666666668,18297663024.0,17.0 GB,nemar,Han2024 – SSVEP fatigue dataset with two frequency paradigms,eeg,"[{""val"": 64, ""count"": 48}]","[{""val"": 1000.0, ""count"": 48}]",CC BY 4.0,,
nm000125,[],author_year,Lee2021_SSVEP,23,85,1,Visual,Perception,Healthy,13.335949999999999,1407989189.0,1.3 GB,nemar,Lee2021 – SSVEP paradigm of the Mobile BCI dataset,eeg,"[{""val"": 73, ""count"": 84}, {""val"": 46, ""count"": 1}]","[{""val"": 100.0, ""count"": 85}]",CC BY 4.0,,
nm000126,[],canonical,Wang2016,34,34,1,Visual,Perception,Healthy,14.506628888888889,3351783823.0,3.1 GB,nemar,Wang2016 – SSVEP Wang 2016 dataset,eeg,"[{""val"": 64, ""count"": 34}]","[{""val"": 250.0, ""count"": 34}]",CC-BY-4.0,,
nm000127,"[""Kim2025""]",author_year,Kim2025_SSVEP,40,240,1,Visual,Perception,Healthy,18.927018229166666,8668702772.0,8.1 GB,nemar,Kim2025 – 40-class beta-range SSVEP speller dataset,eeg,"[{""val"": 31, ""count"": 240}]","[{""val"": 1024.0, ""count"": 240}]",CC BY 4.0,,
nm000128,[],author_year,Dong2023,59,59,1,Visual,Perception,Healthy,14.159934444444444,416400314.0,397.1 MB,nemar,Dong2023 – 59-subject 40-class SSVEP dataset,eeg,"[{""val"": 8, ""count"": 59}]","[{""val"": 250.0, ""count"": 59}]",CC BY-NC 4.0,,
nm000129,"[""BetaSSVEP"", ""BETA_SSVEP"", ""BETA""]",canonical,Liu2020,70,70,1,Visual,Perception,Healthy,13.022144444444445,3012862406.0,2.8 GB,nemar,Liu2020 – BETA SSVEP benchmark dataset,eeg,"[{""val"": 64, ""count"": 70}]","[{""val"": 250.0, ""count"": 70}]",Non-commercial research use,,
nm000130,"[""EldBETA"", ""eldBETA"", ""Liu2022EldBETA""]",canonical,Liu2022,100,700,1,Visual,Perception,Healthy,20.17517222222222,18646340197.0,17.4 GB,nemar,Liu2022 – eldBETA SSVEP benchmark dataset for elderly population,eeg,"[{""val"": 64, ""count"": 700}]","[{""val"": 1000.0, ""count"": 700}]",CC BY 4.0,,
nm000131,[],author_year,Wang2021,8,22,1,Visual,Attention,Healthy,6.1615825,2757289649.0,2.6 GB,nemar,Wang2021 – Combined SSVEP dataset with single stimulus location for two inputs,eeg,"[{""val"": 31, ""count"": 22}]","[{""val"": 1000.0, ""count"": 22}]",CC BY 4.0,,
nm000132,"[""ERPCORE"", ""ERP_CORE""]",canonical,Kappenman2021,40,240,6,,,,37.74583333333333,18809446653.0,17.5 GB,nemar,ERP CORE,eeg,"[{""val"": 33, ""count"": 240}]","[{""val"": 1024, ""count"": 240}]",CC-BY-4.0,10.82901/nemar.nm000132,
nm000133,"[""Alljoined1"", ""Alljoined""]",canonical,Xu2024,8,13,1,,,,,8146889307.0,7.6 GB,nemar,Alljoined1,eeg,"[{""val"": 64, ""count"": 13}]","[{""val"": 512, ""count"": 13}]",CC-BY-NC-ND-4.0,10.82901/nemar.nm000133,
nm000134,"[""Alljoined16M"", ""Alljoined_16M"", ""Alljoined1p6M""]",canonical,Xu2025,20,1525,1,,,,129.4950119357639,8802152182.0,8.2 GB,nemar,Alljoined-1.6M,eeg,"[{""val"": 32, ""count"": 1525}]","[{""val"": 256, ""count"": 1525}]",CC-BY-NC-ND-4.0,10.82901/nemar.nm000134,
nm000135,"[""BNCI2014004""]",canonical,Leeb2014,1,5,1,Visual,Motor,Healthy,2.8521533333333333,23679375.0,22.6 MB,nemar,BNCI 2014-004 Motor Imagery dataset,eeg,"[{""val"": 3, ""count"": 5}]","[{""val"": 250.0, ""count"": 5}]",CC-BY-ND-4.0,,
nm000136,[],author_year,GuttmannFlury2025,31,63,1,Visual,Attention,Healthy,11.223038055555556,7887167781.0,7.3 GB,nemar,GuttmannFlury2025-P300,eeg,"[{""val"": 65, ""count"": 63}]","[{""val"": 1000.0, ""count"": 63}]",CC0,doi:10.1038/s41597-025-04861-9,
nm000137,[],canonical,Kaya2018,7,17,1,Visual,Motor,Healthy,15.696565277777776,653703701.0,623.4 MB,nemar,"Classical motor imagery dataset with left hand, right hand, and rest",eeg,"[{""val"": 19, ""count"": 17}]","[{""val"": 200.0, ""count"": 17}]",CC-BY-4.0,,
nm000138,"[""AlexMI"", ""AlexMotorImagery"", ""AlexandreMotorImagery""]",canonical,Barachant2012,8,8,1,Visual,Motor,Healthy,1.1037152777777777,104556459.0,99.7 MB,nemar,Alex Motor Imagery dataset,eeg,"[{""val"": 16, ""count"": 8}]","[{""val"": 512.0, ""count"": 8}]",CC-BY-SA-4.0,,
nm000139,"[""BNCI2014001"", ""BCICIV1"", ""BCICompIV1""]",canonical,Tangermann2014,9,108,1,Multisensory,Motor,Healthy,11.60808,705462224.0,672.8 MB,nemar,BNCI 2014-001 Motor Imagery dataset,eeg,"[{""val"": 22, ""count"": 108}]","[{""val"": 250.0, ""count"": 108}]",CC-BY-ND-4.0,,
nm000140,"[""BNCI2015"", ""BNCI2015001""]",canonical,Faller2015,12,28,1,Visual,Motor,Healthy,16.68931640625,1212919293.0,1.1 GB,nemar,BNCI 2015-001 Motor Imagery dataset,eeg,"[{""val"": 13, ""count"": 28}]","[{""val"": 512.0, ""count"": 28}]",CC-BY-NC-ND-4.0,,
nm000141,[],author_year,Wairagkar2018,14,14,1,Visual,Motor,Healthy,2.8049180772569446,599428856.0,571.7 MB,nemar,Motor execution dataset from Wairagkar et al 2018,eeg,"[{""val"": 19, ""count"": 14}]","[{""val"": 1024.0, ""count"": 14}]",CC-BY-4.0,,
nm000142,[],author_year,Wu2020,6,13,1,Visual,Motor,Healthy,4.0056075,5290402739.0,4.9 GB,nemar,Ear-EEG motor execution dataset from Wu et al 2020,eeg,"[{""val"": 122, ""count"": 13}]","[{""val"": 1000.0, ""count"": 13}]",CC-BY-4.0,,
nm000143,"[""BCICIII_IVa"", ""BCICompIII_IVa"", ""BNCI2003_IVa""]",canonical,BNCI2003,5,5,1,Visual,Motor,Healthy,3.9763027777777777,516668416.0,492.7 MB,nemar,BNCI2003_IVa Motor Imagery dataset,eeg,"[{""val"": 118, ""count"": 5}]","[{""val"": 100.0, ""count"": 5}]",CC-BY-4.0,,
nm000144,"[""BNCI2015""]",canonical,Scherer2015,9,18,1,Visual,Motor,Other,13.750518663194445,1157280768.0,1.1 GB,nemar,BNCI 2015-004 Mental tasks dataset,eeg,"[{""val"": 30, ""count"": 18}]","[{""val"": 256.0, ""count"": 18}]",CC-BY-NC-ND-4.0,,
nm000145,[],author_year,GrosseWentrup2009,10,10,1,Visual,Motor,Healthy,8.404805555555555,5824656181.0,5.4 GB,nemar,Munich Motor Imagery dataset,eeg,"[{""val"": 128, ""count"": 10}]","[{""val"": 500.0, ""count"": 10}]",CC-BY-4.0,,
nm000146,"[""Weibo2014""]",canonical,Yi2014,10,10,1,Visual,Motor,Healthy,13.080541666666665,1720642221.0,1.6 GB,nemar,Motor Imagery dataset from Weibo et al 2014,eeg,"[{""val"": 60, ""count"": 10}]","[{""val"": 200.0, ""count"": 10}]",CC0-1.0,,
nm000147,"[""Romani2025""]",author_year,RomaniBF2025,22,120,1,Visual,Learning,Healthy,6.27819111111111,140866957.0,134.3 MB,nemar,RomaniBF2025ERP,eeg,"[{""val"": 8, ""count"": 120}]","[{""val"": 250.0, ""count"": 120}]",CC-BY-4.0,doi:10.48550/arXiv.2510.10169,
nm000148,[],author_year,Rozado2015,30,60,1,Auditory,Motor,Healthy,5.7026199001736115,1022640656.0,975.3 MB,nemar,Motor imagery BCI dataset with pupillometry augmentation,eeg,"[{""val"": 32, ""count"": 60}]","[{""val"": 512.0, ""count"": 60}]",CC0 1.0,,
nm000149,[],author_year,Ofner2019,10,90,1,Visual,Motor,Other,7.536291232638889,1289632633.0,1.2 GB,nemar,BNCI 2019-001 Motor Imagery dataset for Spinal Cord Injury patients,eeg,"[{""val"": 61, ""count"": 90}]","[{""val"": 256.0, ""count"": 90}]",CC-BY-4.0,,
nm000150,[],author_year,Liu2025_NEMAR,0,0,0,,,,,0.0,Unknown,nemar,Liu2025 - NEMAR Dataset,eeg,[],[],,,
nm000151,[],author_year,Tavakolan2017,12,46,1,Visual,Motor,Healthy,9.901242777777778,3435333443.0,3.2 GB,nemar,Motor imagery dataset for three imaginary states of the same upper extremity,eeg,"[{""val"": 32, ""count"": 46}]","[{""val"": 1000.0, ""count"": 46}]",CC0-1.0,,
nm000152,[],author_year,Zhang2017,12,180,1,Visual,Motor,Healthy,9.24525,1727304819.0,1.6 GB,nemar,Upper-limb elbow-centered motor imagery dataset (10 classes),eeg,"[{""val"": 17, ""count"": 180}]","[{""val"": 1000.0, ""count"": 180}]",CC BY 4.0,,
nm000155,[],,Caillet2023,6,11,2,,,,0.12277777777777778,470056753.0,448.3 MB,nemar,MUniverse Caillet et al 2023,emg,"[{""val"": 259, ""count"": 11}]","[{""val"": 2048, ""count"": 11}]",CC0 BY 4.0,https://doi.org/10.7910/DVN/F9GWIW,
nm000157,[],,Mainsah2025,19,544,1,,,,28.657737184336803,1302471829.0,1.2 GB,nemar,Mainsah2025-B,eeg,"[{""val"": 16, ""count"": 543}]","[{""val"": 256.00008099666394, ""count"": 205}, {""val"": 256.00004960640416, ""count"": 161}, {""val"": 256.00006152923214, ""count"": 98}, {""val"": 256, ""count"": 42}, {""val"": 256.0000879536484, ""count"": 15}, {""val"": 256.00010844590247, ""count"": 10}, {""val"": 256.00004522469607, ""count"": 3}, {""val"": 256.00012796007644, ""count"": 2}, {""val"": 256.00011633064076, ""count"": 2}, {""val"": 256.0001184842897, ""count"": 2}, {""val"": 256.00007397509813, ""count"": 2}, {""val"": 256.00008239793436, ""count"": 1}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000158,[],author_year,Liu2024,50,50,1,Multisensory,Motor,Other,4.444416666666666,706376579.0,673.7 MB,nemar,Dataset [1]_ from the study on motor imagery [2]_,eeg,"[{""val"": 29, ""count"": 50}]","[{""val"": 500.0, ""count"": 50}]",CC-BY-4.0,,
nm000159,[],,Avrillon2024,16,124,8,,,,1.5522222222222222,5917753373.0,5.5 GB,nemar,MUniverse Avrillon et al 2024,emg,"[{""val"": 258, ""count"": 124}]","[{""val"": 2048, ""count"": 124}]",CC0 BY 4.0,https://doi.org/10.7910/DVN/L9OQY7,
nm000160,[],author_year,Yi2025,18,141,1,Visual,Motor,Healthy,32.48256083333333,21794537131.0,20.3 GB,nemar,Multi-joint upper-limb MI dataset from Yi et al. 2025,eeg,"[{""val"": 62, ""count"": 141}]","[{""val"": 1000.0, ""count"": 141}]",CC-BY-NC-ND-4.0,,
nm000161,[],author_year,Crell2024,20,40,1,Visual,Motor,Healthy,33.61688888888889,10921586386.0,10.2 GB,nemar,BNCI 2024-001 Handwritten Character Classification dataset,eeg,"[{""val"": 60, ""count"": 40}]","[{""val"": 500.0, ""count"": 40}]",CC-BY-4.0,,
nm000162,"[""BNCI2025""]",canonical,Srisrisawang2025,20,20,1,Visual,Motor,Healthy,44.44853333333333,16132682212.0,15.0 GB,nemar,BNCI 2025-001 Motor Kinematics Reaching dataset,eeg,"[{""val"": 67, ""count"": 20}]","[{""val"": 500.0, ""count"": 20}]",CC-BY-4.0,,
nm000163,[],author_year,Castillos2023_VEP,12,12,1,Visual,Attention,Healthy,0.878318888888889,167892325.0,160.1 MB,nemar,c-VEP and Burst-VEP dataset from Castillos et al. (2023),eeg,"[{""val"": 32, ""count"": 12}]","[{""val"": 500.0, ""count"": 12}]",CC-BY-4.0,,
nm000165,[],,Grison2025,1,10,10,,,,0.14916666666666667,1441274754.0,1.3 GB,nemar,MUniverse Grison et al 2025,emg,"[{""val"": 131, ""count"": 10}]","[{""val"": 10240, ""count"": 10}]",CC0 BY 4.0,https://doi.org/10.7910/DVN/ID1WNQ,
nm000166,[],,Huang2018,95,2469,13,,,,100.47502888888889,23207102560.0,21.6 GB,nemar,"M3CV: Multi-subject, Multi-session, Multi-task EEG Database",eeg,"[{""val"": 64, ""count"": 2469}]","[{""val"": 250, ""count"": 2469}]",CC BY 4.0,doi:10.1016/j.neuroimage.2022.119666,
nm000167,[],author_year,Ma2020,25,375,1,Visual,Motor,Healthy,35.204795833333336,24091695012.0,22.4 GB,nemar,Motor imagery dataset from Ma et al. 2020,eeg,"[{""val"": 64, ""count"": 225}, {""val"": 62, ""count"": 150}]","[{""val"": 1000.0, ""count"": 375}]",CC-BY-4.0,,
nm000168,"[""Chavarriaga2010""]",author_year,Chavarriaga2015,6,120,1,Visual,Attention,Healthy,6.0910460069444445,2176366718.0,2.0 GB,nemar,BNCI 2015-013 Error-Related Potentials dataset,eeg,"[{""val"": 64, ""count"": 120}]","[{""val"": 512.0, ""count"": 120}]",CC-BY-NC-ND-4.0,,
nm000169,"[""BNCI2014008""]",canonical,Riccio2014,8,8,1,Visual,Attention,Other,3.018255208333333,79628492.0,75.9 MB,nemar,BNCI 2014-008 P300 dataset (ALS patients),eeg,"[{""val"": 8, ""count"": 8}]","[{""val"": 256.0, ""count"": 8}]",CC-BY-NC-ND-4.0,,
nm000170,"[""BNCI2025""]",canonical,Pulferer2025,10,90,1,Visual,Motor,Other,28.178534722222224,3669211627.0,3.4 GB,nemar,BNCI 2025-002 Continuous 2D Trajectory Decoding dataset,eeg,"[{""val"": 60, ""count"": 90}]","[{""val"": 200.0, ""count"": 90}]",CC-BY-4.0,,
nm000171,"[""BNCI2014002""]",canonical,Steyrl2014,14,112,1,Visual,Motor,Healthy,6.865494791666666,581180985.0,554.3 MB,nemar,BNCI 2014-002 Motor Imagery dataset,eeg,"[{""val"": 15, ""count"": 112}]","[{""val"": 512.0, ""count"": 112}]",CC-BY-ND-4.0,,
nm000172,[],author_year,Schirrmeister2017,14,28,1,Visual,Motor,Healthy,28.695817777777776,19854097472.0,18.5 GB,nemar,High-gamma dataset described in Schirrmeister et al. 2017,eeg,"[{""val"": 128, ""count"": 28}]","[{""val"": 500.0, ""count"": 28}]",CC-BY-4.0,,
nm000173,[],canonical,Ofner2017,15,300,1,Visual,Motor,Healthy,27.10289279513889,9172718452.0,8.5 GB,nemar,Motor Imagery ataset from Ofner et al 2017,eeg,"[{""val"": 61, ""count"": 300}]","[{""val"": 512.0, ""count"": 300}]",CC-BY-4.0,,
nm000175,[],,Luke2024,5,5,1,,,,3.808533333333333,49813520.0,47.5 MB,nemar,fNIRS Finger Tapping,fnirs,"[{""val"": 56, ""count"": 5}]","[{""val"": 7.8125, ""count"": 5}]",CC0,,
nm000176,"[""BigP3BCI_StudyK"", ""BigP3BCI_K""]",canonical,Mainsah2025_BigP3BCI,5,128,1,Visual,Perception,Healthy,3.5955902777777777,176435450.0,168.3 MB,nemar,"BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects)",eeg,"[{""val"": 16, ""count"": 128}]","[{""val"": 256.0, ""count"": 128}]",CC-BY-4.0,,
nm000179,"[""LEMON""]",canonical,Babayan2018,215,215,1,,,,62.371518555555554,136286157674.0,126.9 GB,nemar,LEMON: MPI Leipzig Mind-Brain-Body EEG (Resting State),eeg,"[{""val"": 62, ""count"": 215}]","[{""val"": 2500, ""count"": 208}, {""val"": 1000, ""count"": 6}, {""val"": 500, ""count"": 1}]",CC BY 4.0,doi:10.1038/sdata.2018.308,
nm000180,[],,Brennan2019,45,45,1,,,,9.154141666666668,4087113893.0,3.8 GB,nemar,Brennan2019: EEG during Alice in Wonderland Listening,eeg,"[{""val"": 62, ""count"": 45}]","[{""val"": 500, ""count"": 45}]",CC BY 4.0,doi:10.1371/journal.pone.0207741,
nm000181,[],,Khan2019,2417,2417,1,,,,488.9631958333334,14823368893.0,13.8 GB,nemar,NMT: Neurodiagnostic Montage Template Scalp EEG,eeg,"[{""val"": 21, ""count"": 2417}]","[{""val"": 200, ""count"": 2417}]",CC BY-SA 4.0,doi:10.5281/zenodo.10909103,
nm000185,"[""SleepEDF"", ""SleepEDFExpanded""]",,Kemp2000,100,197,1,,,,3849.036111111111,8716144560.0,8.1 GB,nemar,Sleep-EDF Expanded: Whole-Night PSG Recordings,eeg,"[{""val"": 7, ""count"": 153}, {""val"": 5, ""count"": 44}]","[{""val"": 100, ""count"": 197}]",ODbL v1.0,doi:10.13026/C2X676,
nm000186,"[""BigP3BCI_StudyE"", ""BigP3BCI_E""]",canonical,Mainsah2025_BigP3BCI_E,8,88,1,Visual,Attention,Healthy,2.3882378472222223,109762280.0,104.7 MB,nemar,BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects),eeg,"[{""val"": 16, ""count"": 88}]","[{""val"": 256.0, ""count"": 88}]",CC-BY-4.0,,
nm000187,"[""BigP3BCI_StudyN""]",author_year,Mainsah2025_BigP3BCI_N,8,160,1,Visual,Attention,Other,8.200868055555556,370324207.0,353.2 MB,nemar,BigP3BCI Study N — 9x8 dry/wet electrode comparison (8 ALS subjects),eeg,"[{""val"": 16, ""count"": 160}]","[{""val"": 256.0, ""count"": 160}]",CC-BY-4.0,,
nm000188,"[""BNCI2014_009_P300""]",canonical,Arico2014,10,30,1,Visual,Attention,Healthy,1.6335611979166667,74344053.0,70.9 MB,nemar,BNCI 2014-009 P300 dataset,eeg,"[{""val"": 16, ""count"": 30}]","[{""val"": 256.0, ""count"": 30}]",CC-BY-NC-ND-4.0,,
nm000189,"[""BNCI2015_P300"", ""BNCI2015_003_P300"", ""BNCI2015_003_AMUSE""]",canonical,Schreuder2015_P300,10,20,1,Auditory,Attention,Healthy,0.9342003038194444,22835171.0,21.8 MB,nemar,BNCI 2015-003 P300 dataset,eeg,"[{""val"": 8, ""count"": 20}]","[{""val"": 256.0, ""count"": 20}]",CC-BY-NC-ND-4.0,,
nm000190,"[""BNCI2015""]",canonical,Hohne2015,10,20,1,Auditory,Attention,Healthy,13.575294444444443,2321810370.0,2.2 GB,nemar,BNCI 2015-012 PASS2D P300 dataset,eeg,"[{""val"": 63, ""count"": 20}]","[{""val"": 250.0, ""count"": 20}]",CC-BY-NC-ND-4.0,,
nm000191,"[""BigP3BCI_StudyF"", ""BigP3BCI_F""]",canonical,Mainsah2025_BigP3BCI_F,10,270,1,Visual,Attention,Other,12.812762586805556,578709543.0,551.9 MB,nemar,"BigP3BCI Study F — 6x6 multi-paradigm, 3 sessions (10 healthy subjects)",eeg,"[{""val"": 16, ""count"": 270}]","[{""val"": 256.0, ""count"": 270}]",CC-BY-4.0,,
nm000192,"[""BNCI2015_BNCI_006_Music"", ""BNCI_2015_006_Music"", ""BNCI2015_006_MusicBCI""]",canonical,Treder2015_BNCI_006_Music,11,11,1,Auditory,Attention,Healthy,33.94770694444444,4703111928.0,4.4 GB,nemar,BNCI 2015-006 Music BCI dataset,eeg,"[{""val"": 64, ""count"": 11}]","[{""val"": 200.0, ""count"": 11}]",CC-BY-NC-ND-4.0,,
nm000193,[],author_year,Kojima2024A_P300,11,66,1,Auditory,Attention,Healthy,5.797537222222223,4016738870.0,3.7 GB,nemar,Class for Kojima2024A dataset management. P300 dataset,eeg,"[{""val"": 64, ""count"": 66}]","[{""val"": 1000.0, ""count"": 66}]",CC0-1.0,,
nm000194,"[""BNCI2015""]",canonical,Acqualagna2015,12,24,1,Visual,Attention,Healthy,16.163227777777777,2221728980.0,2.1 GB,nemar,BNCI 2015-010 RSVP P300 dataset,eeg,"[{""val"": 63, ""count"": 22}, {""val"": 61, ""count"": 2}]","[{""val"": 200.0, ""count"": 24}]",CC-BY-NC-ND-4.0,,
nm000195,"[""Huebner2018""]",author_year,Hubner2018,12,360,1,Visual,Attention,Healthy,15.3207975,5168060358.0,4.8 GB,nemar,Mixture of LLP and EM for a visual matrix speller (ERP) dataset from,eeg,"[{""val"": 31, ""count"": 360}]","[{""val"": 1000.0, ""count"": 360}]",CC-BY-4.0,,
nm000196,[],canonical,Thielen2015,12,36,1,Visual,Attention,Healthy,2.6154667154947915,3761971274.0,3.5 GB,nemar,c-VEP dataset from Thielen et al. (2015),eeg,"[{""val"": 64, ""count"": 36}]","[{""val"": 2048.0, ""count"": 36}]",CC0-1.0,,
nm000197,"[""BigP3BCI_StudyM"", ""BigP3BCI_M""]",canonical,Mainsah2025_BigP3BCI_M,21,420,1,Visual,Attention,Other,11.218919270833334,515454350.0,491.6 MB,nemar,BigP3BCI Study M — 9x8 adaptive/checkerboard (21 ALS subjects),eeg,"[{""val"": 16, ""count"": 420}]","[{""val"": 256.0, ""count"": 420}]",CC-BY-4.0,,
nm000198,"[""BNCI2015_P300"", ""BNCI2015_008_P300"", ""BNCI2015_008_CenterSpeller""]",canonical,Treder2015_P300,13,26,1,Visual,Attention,Healthy,19.37079333333333,3308112084.0,3.1 GB,nemar,BNCI 2015-008 Center Speller P300 dataset,eeg,"[{""val"": 63, ""count"": 26}]","[{""val"": 250.0, ""count"": 26}]",CC-BY-NC-ND-4.0,,
nm000199,"[""Huebner2017""]",canonical,Hubner2017,13,342,1,Visual,Attention,Healthy,16.410199166666665,5528194680.0,5.1 GB,nemar,Learning from label proportions for a visual matrix speller (ERP),eeg,"[{""val"": 31, ""count"": 342}]","[{""val"": 1000.0, ""count"": 342}]",CC-BY-4.0,,
nm000200,"[""BigP3BCI_StudyI"", ""BigP3BCI_I""]",canonical,Mainsah2025_BigP3BCI_I,13,265,1,Visual,Attention,Healthy,7.403184678819445,340174595.0,324.4 MB,nemar,BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects),eeg,"[{""val"": 16, ""count"": 265}]","[{""val"": 256.0, ""count"": 265}]",CC-BY-4.0,,
nm000201,[],author_year,Lee2021_ERP,24,113,1,Visual,Attention,Healthy,22.13410722222222,5575356846.0,5.2 GB,nemar,ERP paradigm of the Mobile BCI dataset,eeg,"[{""val"": 48, ""count"": 108}, {""val"": 73, ""count"": 5}]","[{""val"": 500.0, ""count"": 108}, {""val"": 100.0, ""count"": 5}]",CC BY 4.0,,
nm000204,[],author_year,Lee2024_Bluetooth_speaker_14,14,420,1,Visual,Attention,Healthy,1.95331,338663856.0,323.0 MB,nemar,"Bluetooth speaker experiment (14 subjects, 6 classes, 31 EEG ch)",eeg,"[{""val"": 31, ""count"": 420}]","[{""val"": 500.0, ""count"": 420}]",CC-BY-4.0,,
nm000205,[],author_year,Zheng2020,14,84,1,Visual,Attention,Healthy,8.461972777777778,5686833347.0,5.3 GB,nemar,RSVP collaborative BCI dataset from Zheng et al 2020,eeg,"[{""val"": 62, ""count"": 84}]","[{""val"": 1000.0, ""count"": 84}]",CC-BY-4.0,,
nm000206,"[""Hinss2021""]",canonical,Hinss2021_Neuroergonomic,15,30,1,Visual,Attention,Healthy,3.9749833333333333,1310961905.0,1.2 GB,nemar,Neuroergonomic 2021 dataset,eeg,"[{""val"": 61, ""count"": 30}]","[{""val"": 500.0, ""count"": 30}]",CC-BY-SA-4.0,,
nm000207,[],author_year,Kojima2024B_P300,15,180,1,Auditory,Attention,Healthy,21.62847222222222,14976725618.0,13.9 GB,nemar,Class for Kojima2024B dataset management. P300 dataset,eeg,"[{""val"": 64, ""count"": 180}]","[{""val"": 1000.0, ""count"": 180}]",CC0-1.0,,
nm000208,[],author_year,Lee2024_Door_lock_control,14,434,1,Visual,Attention,Healthy,3.671492222222222,639234216.0,609.6 MB,nemar,"Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)",eeg,"[{""val"": 31, ""count"": 434}]","[{""val"": 500.0, ""count"": 434}]",CC-BY-4.0,,
nm000209,[],author_year,Forenzo2023,25,150,1,Visual,Motor,Healthy,7.572991388888889,5253102816.0,4.9 GB,nemar,Motor imagery + spatial attention dataset from Forenzo & He 2023,eeg,"[{""val"": 64, ""count"": 150}]","[{""val"": 1000.0, ""count"": 150}]",CC-BY-4.0,,
nm000210,"[""BCIAUTP300"", ""BCIAUT_P300"", ""BCIAUT""]",canonical,Simoes2020,15,210,1,Visual,Clinical/Intervention,Development,187.43532222222223,4090796514.0,3.8 GB,nemar,BCIAUT-P300 dataset for autism from Simoes et al 2020,eeg,"[{""val"": 8, ""count"": 210}]","[{""val"": 250.0, ""count"": 210}]",CC-BY-4.0,,
nm000211,"[""Zhang2025""]",author_year,Zhang2025_RSVP,15,240,1,Visual,Attention,Healthy,15.022525,9300616154.0,8.7 GB,nemar,RSVP ERP dataset for authentication from Zhang et al 2025,eeg,"[{""val"": 57, ""count"": 240}]","[{""val"": 1000.0, ""count"": 240}]",CC-BY-NC-ND-4.0,,
nm000212,"[""BNCI2015""]",canonical,Schaeff2015,16,32,1,Visual,Attention,Healthy,19.954505555555556,1372915485.0,1.3 GB,nemar,BNCI 2015-007 Motion VEP (mVEP) Speller dataset,eeg,"[{""val"": 63, ""count"": 32}]","[{""val"": 100.0, ""count"": 32}]",CC-BY-NC-ND-4.0,,
nm000213,[],author_year,Lee2024_Television_control_30,30,2300,1,Visual,Attention,Healthy,8.477633333333333,1517440605.0,1.4 GB,nemar,"Television control experiment (30 subjects, 4 classes, 31 EEG ch)",eeg,"[{""val"": 31, ""count"": 2300}]","[{""val"": 500.0, ""count"": 2300}]",CC-BY-4.0,,
nm000214,[],author_year,Thielen2021,30,150,1,Visual,Perception,Healthy,27.764727105034723,1581376842.0,1.5 GB,nemar,c-VEP dataset from Thielen et al. (2021),eeg,"[{""val"": 8, ""count"": 150}]","[{""val"": 512.0, ""count"": 150}]",CC0-1.0,,
nm000215,"[""BrainInvaders2014b"", ""BI2014b"", ""BrainInvadersBI2014b""]",canonical,Korczowski2014_P300,38,38,1,Visual,Attention,Healthy,2.362566189236111,421335599.0,401.8 MB,nemar,"P300 dataset BI2014b from a ""Brain Invaders"" experiment",eeg,"[{""val"": 32, ""count"": 38}]","[{""val"": 512.0, ""count"": 38}]",CC-BY-4.0,,
nm000216,"[""BrainInvaders2015a"", ""BI2015a""]",canonical,Korczowski2015_P300,43,129,1,Visual,Perception,Healthy,11.664148763020833,2080775571.0,1.9 GB,nemar,"P300 dataset BI2015a from a ""Brain Invaders"" experiment",eeg,"[{""val"": 32, ""count"": 129}]","[{""val"": 512.0, ""count"": 129}]",CC-BY-4.0,,
nm000217,"[""BrainInvaders2015b"", ""BI2015b""]",canonical,Korczowski2015_P300_BI2015b,44,176,1,Visual,Attention,Healthy,26.080008680555554,4640673271.0,4.3 GB,nemar,"P300 dataset BI2015b from a ""Brain Invaders"" experiment",eeg,"[{""val"": 32, ""count"": 176}]","[{""val"": 512.0, ""count"": 176}]",CC-BY-4.0,,
nm000218,"[""BigP3BCI_StudyH"", ""BigP3BCI_H""]",canonical,Mainsah2025_BigP3BCI_H,16,372,1,Visual,Attention,Healthy,7.428207465277778,342382842.0,326.5 MB,nemar,BigP3BCI Study H — 9x8 checkerboard with gaze conditions (16 healthy subjects),eeg,"[{""val"": 16, ""count"": 372}]","[{""val"": 256.0, ""count"": 372}]",CC-BY-4.0,,
nm000219,"[""BNCI2020"", ""BNCI2020_002_AttentionShift"", ""BNCI2020_002_CovertSpatialAttention""]",canonical,Reichert2020,18,18,1,Visual,Attention,Healthy,13.226646666666667,1073295300.0,1023.6 MB,nemar,BNCI 2020-002 Attention Shift (Covert Spatial Attention) dataset,eeg,"[{""val"": 30, ""count"": 18}]","[{""val"": 250.0, ""count"": 18}]",CC-BY-4.0,,
nm000221,"[""Alphawaves"", ""Rodrigues2017"", ""AlphaWaves""]",canonical,Cattan2017,19,19,1,Resting State,Resting-state,Healthy,0.9618820529513888,85647715.0,81.7 MB,nemar,Alphawaves dataset,eeg,"[{""val"": 16, ""count"": 19}]","[{""val"": 512.0, ""count"": 19}]",CC-BY-4.0,,
nm000222,[],author_year,Lee2024_Air_conditioner_control,10,305,1,Visual,Attention,Healthy,3.1864966666666668,435428261.0,415.3 MB,nemar,"Air conditioner control experiment (10 subjects, 4 classes, 25 EEG ch)",eeg,"[{""val"": 25, ""count"": 305}]","[{""val"": 500.0, ""count"": 305}]",CC-BY-4.0,,
nm000223,[],author_year,Lee2024_Electric_light_control,15,465,1,Visual,Attention,Healthy,3.895852777777778,663135528.0,632.4 MB,nemar,"Electric light control experiment (15 subjects, 4 classes, 31 EEG ch)",eeg,"[{""val"": 31, ""count"": 465}]","[{""val"": 500.0, ""count"": 465}]",CC-BY-4.0,,
nm000225,[],,Ghassemi2018,1983,1983,1,,,,15261.231134722222,430724636141.0,401.1 GB,nemar,PhysioNet 2018 Challenge: Sleep Arousal Detection PSG (Training),eeg,"[{""val"": 13, ""count"": 1983}]","[{""val"": 200, ""count"": 1983}]",Open Data Commons Attribution License v1.0,doi:10.13026/6phb-r450,
nm000226,"[""Zhou2016_NEMAR""]",canonical,Zhou2016_226,4,24,1,,,,6.271044444444445,553971345.0,528.3 MB,nemar,Zhou2016,eeg,"[{""val"": 14, ""count"": 24}]","[{""val"": 100, ""count"": 24}]",CC-BY-4.0,10.82901/nemar.nm000115,
nm000227,"[""GuttmannFlury2025_ME""]",author_year,GuttmannFlury2025_Eye,31,63,1,Visual,Motor,Healthy,7.093593611111111,5059348126.0,4.7 GB,nemar,Eye-BCI Motor Execution dataset from Guttmann-Flury et al 2025,eeg,"[{""val"": 66, ""count"": 63}]","[{""val"": 1000.0, ""count"": 63}]",CC0,,
nm000228,[],,Nieuwland2018,356,397,2,,,,232.39741483832464,110280713631.0,102.7 GB,nemar,Nieuwland et al. 2018: Multi-site N400 Replication Study,eeg,"[{""val"": 66, ""count"": 81}, {""val"": 32, ""count"": 78}, {""val"": 73, ""count"": 77}, {""val"": 65, ""count"": 43}, {""val"": 41, ""count"": 40}, {""val"": 64, ""count"": 38}, {""val"": 144, ""count"": 37}, {""val"": 138, ""count"": 3}]","[{""val"": 500, ""count"": 122}, {""val"": 512, ""count"": 116}, {""val"": 1000, ""count"": 78}, {""val"": 2048, ""count"": 41}, {""val"": 250, ""count"": 40}]",CC-BY 4.0,doi:10.7554/eLife.33468,
nm000229,"[""MASC_MEG"", ""MEG_MASC""]",canonical,Gwilliams2023,29,1360,79,,,,,0.0,Unknown,nemar,Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing,eeg,"[{""val"": 208, ""count"": 196}]","[{""val"": 1000, ""count"": 196}]",CC0,doi:10.1038/s41597-023-02752-5,
nm000230,[],author_year,Zuo2025,30,118,1,Visual,Motor,Other,38.07771222222222,6175868677.0,5.8 GB,nemar,Lower-limb MI dataset for knee pain patients from Zuo et al. 2025,eeg,"[{""val"": 30, ""count"": 118}]","[{""val"": 500.0, ""count"": 118}]",CC-BY-4.0,,
nm000231,"[""EPFLP300"", ""EPFL_P300"", ""EPFLP300Dataset""]",canonical,Hoffmann2008,8,192,1,Visual,Attention,Other,2.9408072916666668,2086585448.0,1.9 GB,nemar,P300 dataset from Hoffmann et al 2008,eeg,"[{""val"": 32, ""count"": 192}]","[{""val"": 2048.0, ""count"": 192}]",Unknown,,
nm000232,[],,Gifford2019,10,638,5,,,,87.2788263888889,218909667332.0,203.9 GB,nemar,THINGS-EEG2: A large and rich EEG dataset for modeling human visual object recognition,eeg,"[{""val"": 63, ""count"": 319}]","[{""val"": 1000, ""count"": 319}]",CC-BY 4.0,doi:10.17605/OSF.IO/3JK45,
nm000234,"[""BNCI2015_ERP""]",canonical,Schreuder2015_ERP,21,42,1,Auditory,Attention,Healthy,30.17912,4917025135.0,4.6 GB,nemar,BNCI 2015-009 AMUSE (Auditory Multi-class Spatial ERP) dataset,eeg,"[{""val"": 60, ""count"": 42}]","[{""val"": 250.0, ""count"": 42}]",CC-BY-NC-ND-4.0,,
nm000235,"[""GuttmannFlury2025_MIME""]",author_year,GuttmannFlury2025_Eye_BCI,31,63,1,Visual,Motor,Healthy,6.996371388888889,4990038162.0,4.6 GB,nemar,Eye-BCI multimodal MI/ME dataset from Guttmann-Flury et al 2025,eeg,"[{""val"": 66, ""count"": 63}]","[{""val"": 1000.0, ""count"": 63}]",CC0,,
nm000236,[],author_year,Cattan2019_P300,21,2520,1,Visual,Attention,Healthy,4.099188368055556,391425304.0,373.3 MB,nemar,Dataset of an EEG-based BCI experiment in Virtual Reality using P300,eeg,"[{""val"": 16, ""count"": 2520}]","[{""val"": 512.0, ""count"": 2520}]",CC-BY-4.0,,
nm000237,[],author_year,Zhou2021,20,833,1,Visual,Motor,Healthy,90.07259277777777,17139583837.0,16.0 GB,nemar,7-day motor imagery BCI EEG dataset from Zhou et al 2021,eeg,"[{""val"": 41, ""count"": 506}, {""val"": 26, ""count"": 327}]","[{""val"": 500.0, ""count"": 833}]",CC-BY-4.0,,
nm000238,[],,Accou2024,87,4088,366,,,,,0.0,Unknown,nemar,"SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants",eeg,"[{""val"": 64, ""count"": 1}]","[{""val"": 8192, ""count"": 1}]",Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) for the EEG data. Stimuli can only be used for non-commercial purposes.,doi:10.48804/K3VSND,
nm000239,[],author_year,MartinezCagigal2023,16,640,1,Visual,Perception,Healthy,15.095158796296298,821074196.0,783.0 MB,nemar,P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023),eeg,"[{""val"": 16, ""count"": 640}]","[{""val"": 256.0, ""count"": 608}, {""val"": 600.0, ""count"": 32}]",CC-BY-NC-SA-4.0,,
nm000240,"[""FernandezRodriguez2023""]",author_year,FernandezRodriguez2025,16,383,1,Visual,Perception,Healthy,13.408473307291667,668714201.0,637.7 MB,nemar,Checkerboard m-sequence-based c-VEP dataset from,eeg,"[{""val"": 16, ""count"": 383}]","[{""val"": 256.0, ""count"": 383}]",CC-BY-NC-SA-4.0,,
nm000241,[],,Zhang2019,2,18,9,,,,3.836216666666667,2012776064.0,1.9 GB,nemar,CerebroVoice: Bilingual sEEG Speech Dataset,ieeg,"[{""val"": 158, ""count"": 6}, {""val"": 114, ""count"": 6}, {""val"": 228, ""count"": 3}, {""val"": 316, ""count"": 3}]","[{""val"": 200, ""count"": 18}]",CC BY 4.0,doi:10.5281/zenodo.13332808,
nm000242,"[""Gao2026""]",author_year,Gao2026_Visual_imagery_et,22,125,1,Visual,Other,Healthy,98.47829861111111,34047544657.0,31.7 GB,nemar,Visual imagery EEG dataset from Gao et al 2026,eeg,"[{""val"": 32, ""count"": 125}]","[{""val"": 1000.0, ""count"": 125}]",CC-BY-NC-ND-4.0,,
nm000243,"[""BNCI2016"", ""BNCI2016002""]",canonical,Haufe2016,15,15,1,Visual,Motor,Healthy,33.74497916666667,4305271416.0,4.0 GB,nemar,BNCI 2016-002 Emergency Braking during Simulated Driving dataset,eeg,"[{""val"": 59, ""count"": 15}]","[{""val"": 200.0, ""count"": 15}]",CC-BY-NC-ND-4.0,,
nm000244,"[""BrainInvaders2014a"", ""BI2014a""]",canonical,Korczowski2014_P300_BI2014a,64,64,1,Visual,Attention,Healthy,12.4046875,1106792754.0,1.0 GB,nemar,"P300 dataset BI2014a from a ""Brain Invaders"" experiment",eeg,"[{""val"": 16, ""count"": 64}]","[{""val"": 512.0, ""count"": 64}]",CC-BY-4.0,,
nm000245,[],canonical,Cho2017,52,52,1,Visual,Motor,Healthy,20.45552734375,7243985570.0,6.7 GB,nemar,Motor Imagery dataset from Cho et al 2017,eeg,"[{""val"": 64, ""count"": 52}]","[{""val"": 512.0, ""count"": 52}]",CC-BY-4.0,,
nm000246,"[""WBCIC_SHU"", ""WBCICSHU""]",canonical,Yang2025_Multi,51,153,1,Visual,Motor,Healthy,98.42606861111109,62733839084.0,58.4 GB,nemar,Multi-day MI-BCI dataset (WBCIC-SHU) from Yang et al 2025,eeg,"[{""val"": 59, ""count"": 153}]","[{""val"": 1000.0, ""count"": 153}]",CC-BY-4.0,,
nm000247,"[""BigP3BCI_StudyS1"", ""BigP3BCI_S1""]",canonical,Mainsah2025_BigP3BCI_S1,10,120,1,Visual,Attention,Healthy,5.566534792017462,501121502.0,477.9 MB,nemar,BigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects),eeg,"[{""val"": 32, ""count"": 120}]","[{""val"": 256.0000766323896, ""count"": 120}]",CC-BY-4.0,,
nm000248,[],author_year,Mainsah2025_BigP3BCI_L,11,330,1,Visual,Attention,Other,18.057970677922476,818465206.0,780.5 MB,nemar,BigP3BCI Study L — 6x6 multi-paradigm (11 ALS subjects),eeg,"[{""val"": 16, ""count"": 330}]","[{""val"": 256.00005870719417, ""count"": 220}, {""val"": 256.0000825640258, ""count"": 110}]",CC-BY-4.0,,
nm000249,"[""Jao2020""]",author_year,Jao2022,13,13,1,Visual,Attention,Healthy,16.191097005208334,3203178899.0,3.0 GB,nemar,BNCI 2022-001 EEG Correlates of Difficulty Level dataset,eeg,"[{""val"": 64, ""count"": 13}]","[{""val"": 256.0, ""count"": 13}]",CC-BY-4.0,,
nm000250,[],author_year,Dreyer2023,87,520,1,Visual,Motor,Healthy,63.4652734375,9489882869.0,8.8 GB,nemar,Class for Dreyer2023 dataset management. MI dataset,eeg,"[{""val"": 27, ""count"": 520}]","[{""val"": 512.0, ""count"": 520}]",CC-BY-4.0,,
nm000251,[],,He2025,1,6,3,,,,1.1308316666666667,2030928007.0,1.9 GB,nemar,"He et al. 2025 — VocalMind: A Stereotactic EEG Dataset for Vocalized, Mimed, and Imagined Speech in Tonal Language",ieeg,"[{""val"": 110, ""count"": 6}]","[{""val"": 1000, ""count"": 6}]",CC BY 4.0,doi:10.1038/s41597-025-04741-2,
nm000253,"[""BrainTreeBank""]",canonical,Wang2024_et_al_Brain,10,26,1,,,,1.8153209092881943,276287106281.0,257.3 GB,nemar,Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli,ieeg,"[{""val"": 164, ""count"": 8}, {""val"": 156, ""count"": 3}, {""val"": 166, ""count"": 3}, {""val"": 190, ""count"": 3}, {""val"": 136, ""count"": 3}, {""val"": 248, ""count"": 2}, {""val"": 218, ""count"": 2}, {""val"": 108, ""count"": 1}, {""val"": 158, ""count"": 1}]","[{""val"": 2048, ""count"": 26}]",CC BY 4.0,doi:10.48550/arXiv.2411.08343,
nm000254,[],,Telesford2024,22,942,12,,,,108.65814155555555,274885989852.0,256.0 GB,nemar,Naturalistic viewing: An open-access dataset using simultaneous EEG-fMRI,eeg,"[{""val"": 64, ""count"": 942}]","[{""val"": 5000, ""count"": 942}]",,,
nm000255,[],,Madsen2024_E2,30,291,5,,,,0.04041666666666666,5663982219.0,5.3 GB,nemar,"The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 2",eeg,"[{""val"": 64, ""count"": 291}]","[{""val"": 128, ""count"": 291}]",CC BY 4.0,,
nm000256,[],,Madsen2024_E3,29,332,6,,,,0.04611111111111111,8050070643.0,7.5 GB,nemar,"The Brain, Body, and Behaviour Dataset (1.0.0) - Experiment 3",eeg,"[{""val"": 64, ""count"": 332}]","[{""val"": 128, ""count"": 332}]",CC BY 4.0,,
nm000259,[],author_year,Speier2017,10,60,1,Visual,Attention,Healthy,3.3766015625,304324391.0,290.2 MB,nemar,Speier2017,eeg,"[{""val"": 32, ""count"": 60}]","[{""val"": 256.0, ""count"": 60}]",CC0,doi:10.1371/journal.pone.0175382,
nm000260,"[""BI2012"", ""BrainInvaders""]",canonical,BrainInvaders2012,23,46,1,Visual,Attention,Healthy,7.122400173611111,172784440.0,164.8 MB,nemar,BrainInvaders2012,eeg,"[{""val"": 17, ""count"": 46}]","[{""val"": 128.0, ""count"": 46}]",CC-BY-4.0,doi:10.5281/zenodo.2649006,
nm000264,"[""BrainInvaders2013a"", ""BI2013a""]",canonical,BrainInvaders2013,24,292,1,Visual,Attention,Healthy,20.632897135416666,1842015606.0,1.7 GB,nemar,BrainInvaders2013a,eeg,"[{""val"": 16, ""count"": 292}]","[{""val"": 512.0, ""count"": 292}]",CC-BY-1.0,doi:10.5281/zenodo.1494163,
nm000265,[],author_year,GuttmannFlury2025_MI,31,126,1,Visual,Motor,Healthy,14.089965000000001,9896626772.0,9.2 GB,nemar,GuttmannFlury2025-MI,eeg,"[{""val"": 65, ""count"": 126}]","[{""val"": 1000.0, ""count"": 126}]",CC0,doi:10.1038/s41597-025-04861-9,
nm000266,[],author_year,Sosulski2019,13,1060,1,Auditory,Attention,Healthy,9.793594444444444,3945575147.0,3.7 GB,nemar,Sosulski2019,eeg,"[{""val"": 37, ""count"": 1060}]","[{""val"": 1000.0, ""count"": 1060}]",CC-BY-SA-4.0,doi:10.48550/arXiv.2109.06011,
nm000267,"[""Shin2017A""]",canonical,Shin2017_Shin2017A,29,174,1,Visual,Motor,Healthy,29.033369444444446,2014295369.0,1.9 GB,nemar,Shin2017A,eeg,"[{""val"": 32, ""count"": 174}]","[{""val"": 200.0, ""count"": 174}]",GPL-3.0,doi:10.1109/TNSRE.2016.2628057,
nm000268,"[""Shin2017B""]",canonical,Shin2017_Shin2017B,29,174,1,Visual,Memory,Healthy,29.033369444444446,2014258770.0,1.9 GB,nemar,Shin2017B,eeg,"[{""val"": 32, ""count"": 174}]","[{""val"": 200.0, ""count"": 174}]",GPL-3.0,doi:10.1109/TNSRE.2016.2628057,
nm000270,[],author_year,Liu2025,27,797,3,Unknown,Motor,Unknown,8.997747222222221,0.0,Unknown,nemar,liu2025 - NEMAR Dataset,eeg,"[{""val"": 64, ""count"": 110}]","[{""val"": 1000, ""count"": 110}]",,,
nm000271,"[""Chang2025""]",author_year,Chang2025_2,28,1245,3,Visual,Motor,Unknown,5.824969444444444,0.0,Unknown,nemar,chang2025 - NEMAR Dataset,eeg,"[{""val"": 59, ""count"": 113}]","[{""val"": 1000, ""count"": 113}]",,,
nm000272,"[""Romani2025_erp""]",author_year,Romani2025_BF_ERP,22,1022,3,Visual,Attention,Unknown,6.2782,0.0,Unknown,nemar,romani-bf2025-erp - NEMAR Dataset,eeg,"[{""val"": 8, ""count"": 120}]","[{""val"": 250, ""count"": 120}]",,,
nm000277,"[""BigP3BCI_G"", ""BigP3BCI_StudyG""]",canonical,Mainsah2025_G,20,320,1,Visual,Attention,Healthy,7.619930555555555,349344096.0,333.2 MB,nemar,Mainsah2025-G,eeg,"[{""val"": 16, ""count"": 320}]","[{""val"": 256.0, ""count"": 320}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000301,[],author_year,Mainsah2025_D,17,307,1,Visual,Attention,Healthy,8.574389105902778,773918371.0,738.1 MB,nemar,Mainsah2025-D,eeg,"[{""val"": 32, ""count"": 307}]","[{""val"": 256.0, ""count"": 307}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000303,[],author_year,Mainsah2025_O,18,347,1,Visual,Perception,Other,11.497953407383674,1040423918.0,992.2 MB,nemar,Mainsah2025-O,eeg,"[{""val"": 32, ""count"": 347}]","[{""val"": 256.0000930697907, ""count"": 162}, {""val"": 256.0, ""count"": 101}, {""val"": 256.00008203487505, ""count"": 40}, {""val"": 256.0001098418278, ""count"": 12}, {""val"": 256.00012071918457, ""count"": 12}, {""val"": 256.00010076264726, ""count"": 7}, {""val"": 256.0001184842897, ""count"": 4}, {""val"": 256.00009694678226, ""count"": 3}, {""val"": 256.00010663894057, ""count"": 3}, {""val"": 256.00008886963377, ""count"": 2}, {""val"": 256.00010131094785, ""count"": 1}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000310,[],author_year,GuttmannFlury2025_SSVEP,11,26,1,Visual,Perception,Healthy,3.1566594444444447,2217198330.0,2.1 GB,nemar,GuttmannFlury2025-SSVEP,eeg,"[{""val"": 65, ""count"": 26}]","[{""val"": 1000.0, ""count"": 26}]",CC0,doi:10.1038/s41597-025-04861-9,
nm000311,[],author_year,Jeong2020,25,213,1,Visual,Motor,Healthy,124.06438472222221,95161244204.0,88.6 GB,nemar,Multimodal upper-limb MI/ME EEG (Jeong et al. 2020),eeg,"[{""val"": 71, ""count"": 213}]","[{""val"": 1000.0, ""count"": 213}]",CC0-1.0,10.82901/nemar.nm000311,
nm000313,[],author_year,Mainsah2025_S2,24,288,1,Visual,Perception,Healthy,13.359683500841909,1202592690.0,1.1 GB,nemar,Mainsah2025-S2,eeg,"[{""val"": 32, ""count"": 288}]","[{""val"": 256.0000766323896, ""count"": 288}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000321,[],author_year,Mainsah2025_Q,36,360,1,Visual,Clinical/Intervention,Other,13.139604984576739,1189232135.0,1.1 GB,nemar,Mainsah2025-Q,eeg,"[{""val"": 32, ""count"": 360}]","[{""val"": 256.0000930697907, ""count"": 208}, {""val"": 256.00008203487505, ""count"": 52}, {""val"": 256.0, ""count"": 43}, {""val"": 256.00010076264726, ""count"": 16}, {""val"": 256.0001098418278, ""count"": 12}, {""val"": 256.00012071918457, ""count"": 12}, {""val"": 256.0001184842897, ""count"": 7}, {""val"": 256.00008886963377, ""count"": 4}, {""val"": 256.00009694678226, ""count"": 3}, {""val"": 256.00010663894057, ""count"": 3}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000323,"[""OpenBMI_ERP"", ""OpenBMI_P300""]",canonical,Lee2019_ERP,54,216,1,Visual,Attention,Healthy,58.12466222222223,41482270421.0,38.6 GB,nemar,Lee2019-ERP,eeg,"[{""val"": 66, ""count"": 216}]","[{""val"": 1000.0, ""count"": 216}]",GPL-3.0,doi:10.1093/gigascience/giz002,
nm000326,[],author_year,Mainsah2025_C,19,341,1,Visual,Attention,Healthy,14.678791922980263,1321765161.0,1.2 GB,nemar,Mainsah2025-C,eeg,"[{""val"": 32, ""count"": 341}]","[{""val"": 256.0000775610915, ""count"": 284}, {""val"": 256.0001218685495, ""count"": 57}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000329,[],canonical,Brandl2020,16,112,1,Auditory,Motor,Healthy,97.11163555555555,66090669820.0,61.6 GB,nemar,Brandl2020,eeg,"[{""val"": 63, ""count"": 112}]","[{""val"": 1000.0, ""count"": 112}]",CC-BY-NC-ND-4.0,doi:10.3389/fnins.2020.566147,
nm000336,[],author_year,Mainsah2025_R,20,480,1,Visual,Attention,Other,23.526139376380513,2117757355.0,2.0 GB,nemar,Mainsah2025-R,eeg,"[{""val"": 32, ""count"": 480}]","[{""val"": 256.0000579103764, ""count"": 249}, {""val"": 256.00011324306917, ""count"": 132}, {""val"": 256.00009140820043, ""count"": 39}, {""val"": 256.0000766323896, ""count"": 27}, {""val"": 256.000065968772, ""count"": 18}, {""val"": 256.0, ""count"": 15}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000338,"[""OpenBMI_MI""]",canonical,Lee2019_MI,54,216,1,Visual,Motor,Healthy,91.54160666666667,65269270841.0,60.8 GB,nemar,Lee2019-MI,eeg,"[{""val"": 66, ""count"": 216}]","[{""val"": 1000.0, ""count"": 216}]",GPL-3.0,doi:10.1093/gigascience/giz002,
nm000339,[],canonical,Stieger2021,62,598,1,Visual,Learning,Healthy,615.3526116666666,398846614515.0,371.5 GB,nemar,Stieger2021,eeg,"[{""val"": 60, ""count"": 598}]","[{""val"": 1000.0, ""count"": 598}]",CC-BY-NC-4.0,doi:10.1038/s41597-021-00883-1,
nm000340,[],author_year,Mainsah2025_J,20,502,1,Visual,Attention,Healthy,9.916121961805555,454670385.0,433.6 MB,nemar,Mainsah2025-J,eeg,"[{""val"": 16, ""count"": 502}]","[{""val"": 256.0, ""count"": 502}]",CC-BY-4.0,doi:10.13026/0byy-ry86,
nm000341,[],author_year,Cattan2019_PHMD,12,12,1,Auditory,Resting-state,Healthy,2.7361046006944445,242533802.0,231.3 MB,nemar,Cattan2019-PHMD,eeg,"[{""val"": 16, ""count"": 12}]","[{""val"": 512.0, ""count"": 12}]",CC-BY-4.0,doi:10.5281/zenodo.2617084,
nm000342,"[""CastillosCVEP40""]",canonical,Castillos2023_CastillosCVEP40,12,12,1,Visual,Attention,Healthy,0.8488822222222221,152372062.0,145.3 MB,nemar,CastillosCVEP40,eeg,"[{""val"": 32, ""count"": 12}]","[{""val"": 500.0, ""count"": 12}]",CC-BY-4.0,doi:10.1016/j.neuroimage.2023.120446,
nm000343,"[""Hinss2021_v2""]",author_year,Hinss2021,15,30,1,Visual,Attention,Healthy,3.9749833333333333,1310961731.0,1.2 GB,nemar,Hinss2021,eeg,"[{""val"": 61, ""count"": 30}]","[{""val"": 500.0, ""count"": 30}]",CC-BY-SA-4.0,doi:10.1038/s41597-022-01898-y,
nm000344,[],author_year,Castillos2023_CastillosBurstVEP100,12,12,1,Visual,Attention,Healthy,0.8772155555555554,157338487.0,150.0 MB,nemar,CastillosBurstVEP100,eeg,"[{""val"": 32, ""count"": 12}]","[{""val"": 500.0, ""count"": 12}]",CC-BY-4.0,doi:10.1016/j.neuroimage.2023.120446,
nm000345,[],author_year,Castillos2023_CastillosBurstVEP40,12,12,1,Visual,Attention,Healthy,0.8422155555555555,151192839.0,144.2 MB,nemar,CastillosBurstVEP40,eeg,"[{""val"": 32, ""count"": 12}]","[{""val"": 500.0, ""count"": 12}]",CC-BY-4.0,doi:10.1016/j.neuroimage.2023.120446,
nm000346,[],author_year,Castillos2023_CastillosCVEP100,12,12,1,Visual,Attention,Healthy,0.880271111111111,157918853.0,150.6 MB,nemar,CastillosCVEP100,eeg,"[{""val"": 32, ""count"": 12}]","[{""val"": 500.0, ""count"": 12}]",CC-BY-4.0,doi:10.1016/j.neuroimage.2023.120446,
nm000347,"[""HEFMI_ICH"", ""HEFMIICH""]",canonical,HefmiIch2025,37,98,1,Multisensory,Motor,Other,31.19656032986111,2766220854.0,2.6 GB,nemar,HefmiIch2025,eeg,"[{""val"": 32, ""count"": 98}]","[{""val"": 256.0, ""count"": 98}]",CC-BY-NC-ND-4.0,doi:10.1038/s41597-025-06100-7,
nm000348,"[""Yang2025""]",author_year,Yang2025,51,153,1,Visual,Motor,Healthy,98.42606861111109,68049094604.0,63.4 GB,nemar,Yang2025,eeg,"[{""val"": 64, ""count"": 153}]","[{""val"": 1000.0, ""count"": 153}]",CC-BY-4.0,doi:10.1038/s41597-025-04826-y,
nm000351,[],author_year,Mainsah2025_P,19,228,1,Visual,Attention,Other,17.685858862826244,1596682427.0,1.5 GB,nemar,Mainsah2025-P,eeg,"[{""val"": 32, ""count"": 228}]","[{""val"": 256.0000930697907, ""count"": 152}, {""val"": 256.00008203487505, ""count"": 38}, {""val"": 256.0, ""count"": 22}, {""val"": 256.00009466146327, ""count"": 1}, {""val"": 256.0001237687188, ""count"": 1}, {""val"": 256.00011666197287, ""count"": 1}, {""val"": 256.00009240629606, ""count"": 1}, {""val"": 256.0001196052653, ""count"": 1}, {""val"": 256.0001141647201, ""count"": 1}, {""val"": 256.0000983717175, ""count"": 1}, {""val"": 256.00007259581656, ""count"": 1}, {""val"": 256.00008694547637, ""count"": 1}, {""val"": 256.0000896869156, ""count"": 1}, {""val"": 256.0001109005733, ""count"": 1}, {""val"": 256.00011860780387, ""count"": 1}, {""val"": 256.00008676866054, ""count"": 1}, {""val"": 256.00009044741086, ""count"": 1}, {""val"": 256.00006037203326, ""count"": 1}, {""val"": 256.0000936228914, ""count"": 1}]",CC-BY-4.0,doi:10.13026/0byy-ry86,