| .. 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 |
|
|
| <div class="api-hero"> |
|
|
| .. 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 |
|
|
| <p class="api-intro"> |
| There are 4 main concepts in the MOABB: |
| <strong class="concept-dataset">the datasets</strong>, |
| <strong class="concept-paradigm">the paradigms</strong>, |
| <strong class="concept-evaluation">the evaluations</strong>, and |
| <strong class="concept-pipeline">the pipelines</strong>. |
| In addition, we offer <strong>statistical</strong>, |
| <strong>visualization</strong>, <strong>utilities</strong> to simplify the workflow. |
| </p> |
| <p class="api-intro"> |
| And if you want to just run the benchmark, you can use our |
| <strong>benchmark</strong> module that wraps all the steps in a single function. |
| </p> |
| </div> |
|
|
|
|
| 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 <http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html>`__ |
| 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 |
|
|
