.. meta:: :description: MOABB API reference - Datasets, Paradigms, Evaluations, Pipelines, and analysis tools for EEG-based BCI benchmarking in Python. :keywords: MOABB API, BCI Python API, EEG analysis, motor imagery paradigm, P300 paradigm, cross-session evaluation ===================== API and Main Concepts ===================== .. raw:: html
.. figure:: images/architecture.svg :alt: Concept flow in MOABB :class: api-architecture-diagram Datasets and Paradigms define the problem; Evaluations and Pipelines define the measurement. .. raw:: html

There are 4 main concepts in the MOABB: the datasets, the paradigms, the evaluations, and the pipelines. In addition, we offer statistical, visualization, utilities to simplify the workflow.

And if you want to just run the benchmark, you can use our benchmark module that wraps all the steps in a single function.

Datasets -------- .. currentmodule:: moabb.datasets A dataset handles and abstracts low-level access to the data. The dataset will read data stored locally, in the format in which they have been downloaded, and will convert them into an MNE raw object. There are options to pool all the different recording sessions per subject or to evaluate them separately. ---------------------- Motor Imagery Datasets ---------------------- .. autosummary:: :toctree: generated/ :template: class.rst AlexMI BNCI2003_004 BNCI2014_001 BNCI2014_002 BNCI2014_004 BNCI2015_001 BNCI2015_004 BNCI2019_001 BNCI2020_001 BNCI2022_001 BNCI2024_001 BNCI2025_001 BNCI2025_002 Cho2017 Dreyer2023 Dreyer2023A Dreyer2023B Dreyer2023C Lee2019_MI GrosseWentrup2009 Ofner2017 PhysionetMI Schirrmeister2017 Shin2017A Shin2017B Weibo2014 Zhou2016 Stieger2021 Liu2024 Beetl2021_A Beetl2021_B BCIComp2020UpperLimb Brandl2020 Chang2025 Forenzo2023 Gao2026 GuttmannFlury2025_ME GuttmannFlury2025_MI HefmiIch2025 Jeong2020 Kaya2018 Kumar2024 Liu2025 Ma2020 Rozado2015 Tavakolan2017 TrianaGuzman2024 Wairagkar2018 Wu2020 Yang2025 Yi2025 Zhang2017 Zhou2020 Zuo2025 ------------------------ Imagined Speech Datasets ------------------------ MOABB now welcomes **imagined speech** datasets — subjects silently imagine speaking words, phonemes, or phrases. They share the ``imagery`` paradigm tag with motor imagery and can be decoded with the existing :class:`~moabb.paradigms.MotorImagery` and :class:`~moabb.paradigms.FilterBankMotorImagery` classes. .. autosummary:: :toctree: generated/ :template: class.rst AguileraRodriguez2025 Nguyen2017_L Nguyen2017_S Nguyen2017_SL Nguyen2017_V Nieto2022 Pressel2016 ----------------- ERP/P300 Datasets ----------------- .. autosummary:: :toctree: generated/ :template: class.rst BI2012 BI2013a BI2014a BI2014b BI2015a BI2015b Cattan2019_VR BNCI2014_008 BNCI2014_009 BNCI2015_003 BNCI2015_006 BNCI2015_007 BNCI2015_008 BNCI2015_009 BNCI2015_010 BNCI2015_012 BNCI2015_013 BNCI2016_002 BNCI2020_002 EPFLP300 Huebner2017 Huebner2018 Lee2019_ERP Sosulski2019 ErpCore2021_ERN ErpCore2021_LRP ErpCore2021_MMN ErpCore2021_N2pc ErpCore2021_N170 ErpCore2021_N400 ErpCore2021_P3 RomaniBF2025ERP Kojima2024A Kojima2024B Lee2021Mobile_ERP Chailloux2020 GuttmannFlury2025_P300 Kaneshiro2015 Lee2024_AC Lee2024_BS Lee2024_DL Lee2024_EL Lee2024_TV Mainsah2025_A Mainsah2025_B Mainsah2025_C Mainsah2025_D Mainsah2025_E Mainsah2025_F Mainsah2025_G Mainsah2025_H Mainsah2025_I Mainsah2025_J Mainsah2025_K Mainsah2025_L Mainsah2025_M Mainsah2025_N Mainsah2025_O Mainsah2025_P Mainsah2025_Q Mainsah2025_R Mainsah2025_S1 Mainsah2025_S2 Simoes2020 Speier2017 Zhang2025 Zheng2020 BCIComp2020WalkingERP -------------- SSVEP Datasets -------------- .. autosummary:: :toctree: generated/ :template: class.rst Kalunga2016 Nakanishi2015 Wang2016 MAMEM1 MAMEM2 MAMEM3 Lee2019_SSVEP Chen2017SingleFlicker Dong2023 Han2024Fatigue Kim2025BetaRange Lee2021Mobile_SSVEP Liu2020BETA Liu2022EldBETA Wang2021Combined GuttmannFlury2025_SSVEP -------------- c-VEP Datasets -------------- .. autosummary:: :toctree: generated/ :template: class.rst Thielen2015 Thielen2021 CastillosBurstVEP40 CastillosBurstVEP100 CastillosCVEP40 CastillosCVEP100 MartinezCagigal2023Checker MartinezCagigal2023Pary ---------------------- Resting State Datasets ---------------------- .. autosummary:: :toctree: generated/ :template: class.rst Cattan2019_PHMD Hinss2021 Rodrigues2017 ----------------- Compound Datasets ----------------- .. currentmodule:: moabb.datasets.compound_dataset .. autosummary:: :toctree: generated/ :template: class.rst BI2014a_Il BI2014b_Il BI2015a_Il BI2015b_Il Cattan2019_VR_Il BI_Il --------- Utilities --------- .. currentmodule:: moabb.datasets .. autosummary:: :toctree: generated/ :template: class.rst base.BaseDataset base.BaseBIDSDataset base.LocalBIDSDataset base.CacheConfig bnci.base.MNEBNCI bnci.base.BNCIBaseDataset erpcore2021.ErpCore2021 mainsah2025.Mainsah2025 lee2024.Lee2024 Lee2019.Lee2019 castillos2023.BaseCastillos2023 ssvep_mamem.BaseMAMEM lee2021_mobile.Lee2021Mobile bbci_eeg_fnirs.BaseShin2017 dreyer2023._Dreyer2023Base huebner_llp._BaseVisualMatrixSpellerDataset compound_dataset.base.CompoundDataset compound_dataset.bi_illiteracy._base_bi_il fake.FakeDataset fake.FakeVirtualRealityDataset .. autosummary:: :toctree: generated/ :template: function.rst download.data_path download.data_dl download.fs_issue_request download.fs_get_file_list download.fs_get_file_hash download.fs_get_file_id download.fs_get_file_name utils.dataset_search utils.find_intersecting_channels utils.plot_datasets_grid utils.plot_datasets_cluster Paradigms --------- .. currentmodule:: moabb.paradigms A paradigm defines how the raw data will be converted to trials ready to be processed by a decoding algorithm. This is a function of the paradigm used, i.e. in motor imagery one can have two-class, multi-class, or continuous paradigms; similarly, different preprocessing is necessary for ERP vs ERD paradigms. ----------------- Imagery Paradigms ----------------- The ``imagery`` paradigm tag covers both motor imagery and imagined speech. :class:`Imagery` is a thin alias for :class:`MotorImagery` that makes the umbrella scope explicit; :class:`SpeechImagery` overrides the defaults with the broadband 1-100 Hz filter used in imagined-speech work (Aguilera-Rodriguez et al. 2025). .. autosummary:: :toctree: generated/ :template: class.rst Imagery SpeechImagery MotorImagery LeftRightImagery FilterBankLeftRightImagery FilterBankMotorImagery -------------- P300 Paradigms -------------- .. autosummary:: :toctree: generated/ :template: class.rst P300 --------------- SSVEP Paradigms --------------- .. autosummary:: :toctree: generated/ :template: class.rst SSVEP FilterBankSSVEP --------------- c-VEP Paradigms --------------- .. autosummary:: :toctree: generated/ :template: class.rst CVEP FilterBankCVEP ----------------------- Resting State Paradigms ----------------------- .. autosummary:: :toctree: generated/ :template: class.rst RestingStateToP300Adapter ----------------------------------- Fixed Interval Windows Processings ----------------------------------- .. autosummary:: :toctree: generated/ :template: class.rst FixedIntervalWindowsProcessing FilterBankFixedIntervalWindowsProcessing --------- Utilities --------- .. autosummary:: :toctree: generated/ :template: class.rst motor_imagery.BaseMotorImagery p300.BaseP300 ssvep.BaseSSVEP BaseFixedIntervalWindowsProcessing base.BaseParadigm base.BaseProcessing Evaluations ----------- .. currentmodule:: moabb.evaluations An evaluation defines how we go from trials per subject and session to a generalization statistic (AUC score, f-score, accuracy, etc) – it can be either within-recording-session accuracy, across-session within-subject accuracy, across-subject accuracy, or other transfer learning settings. .. autosummary:: :toctree: generated/ :template: class.rst WithinSessionEvaluation CrossSessionEvaluation CrossSubjectEvaluation .. autosummary:: :toctree: generated/ :template: class.rst WithinSessionSplitter WithinSubjectSplitter CrossSessionSplitter CrossSubjectSplitter --------- Utilities --------- .. autosummary:: :toctree: generated/ :template: class.rst base.BaseEvaluation Pipelines --------- .. currentmodule:: moabb.pipelines Pipeline defines all steps required by an algorithm to obtain predictions. Pipelines are typically a chain of sklearn compatible transformers and end with a sklearn compatible estimator. See `Pipelines `__ for more info. .. autosummary:: :toctree: generated/ :template: class.rst features.LogVariance features.FM features.ExtendedSSVEPSignal features.AugmentedDataset features.StandardScaler_Epoch csp.TRCSP classification.SSVEP_CCA classification.SSVEP_TRCA classification.SSVEP_MsetCCA classification.SSVEP_itCCA classification.SSVEP_eCCA classification.SSVEP_TRCA_R classification.SSVEP_SSCOR classification.SSVEP_TDCA Statistics, visualization and utilities --------------------------------------- .. currentmodule:: moabb.analysis Once an evaluation has been run, the raw results are returned as a DataFrame. This can be further processed via the following commands to generate some basic visualization and statistical comparisons: -------- Plotting -------- .. autosummary:: :toctree: generated/ :template: function.rst plotting.score_plot plotting.paired_plot plotting.summary_plot plotting.meta_analysis_plot plotting.dataset_bubble_plot ---------- Statistics ---------- .. autosummary:: :toctree: generated/ :template: function.rst meta_analysis.find_significant_differences meta_analysis.compute_dataset_statistics meta_analysis.combine_effects meta_analysis.combine_pvalues meta_analysis.collapse_session_scores ----- Utils ----- .. currentmodule:: moabb .. autosummary:: :toctree: generated/ :template: function.rst set_log_level setup_seed set_download_dir make_process_pipelines Benchmark --------- .. currentmodule:: moabb The benchmark module wraps all the steps in a single function. It downloads the data, runs the benchmark, and returns the results. It is the easiest way to run a benchmark. .. admonition:: Minimal benchmark example .. code-block:: python from moabb import benchmark results = benchmark( pipelines="./pipelines", evaluations=["WithinSession"], paradigms=["LeftRightImagery"], include_datasets=[BNCI2014_001(), PhysionetMI()], exclude_datasets=None, results="./results/", overwrite=True, plot=True, output="./benchmark/", n_jobs=-1, ) .. autosummary:: :toctree: generated/ :template: function.rst benchmark