{"dataset":{"id":"154","dataset_id":"nm000139","name":"BNCI 2014-001 Motor Imagery dataset","description":"The BNCI 2014-001 Motor Imagery dataset is a widely-used benchmark for brain-computer interface research, comprising EEG recordings from 9 healthy subjects performing four-class motor imagery tasks (left hand, right hand, feet, and tongue). Each subject completed two sessions with 6 runs per session, yielding 200 training and 240 test trials. The dataset features 22 EEG channels plus 3 EOG channels (25 total) sampled at 250 Hz with minimal preprocessing (bandpass filtering 0.05-200 Hz), making it a standard resource for evaluating multi-class motor imagery classification algorithms and cross-session transfer learning approaches.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000139","concept_doi":"10.82901/nemar.nm000139","latest_version_doi":"10.82901/nemar.nm000139.v1.0.2","created_at":"2026-03-17 21:59:55","updated_at":"2026-08-18 18:14:38","zenodo_concept_id":"19632020","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"BNCI 2014-001 Motor Imagery dataset\",\n  \"description\": \"The BNCI 2014-001 Motor Imagery dataset is a widely-used benchmark for brain-computer interface research, comprising EEG recordings from 9 healthy subjects performing four-class motor imagery tasks (left hand, right hand, feet, and tongue). Each subject completed two sessions with 6 runs per session, yielding 200 training and 240 test trials. The dataset features 22 EEG channels plus 3 EOG channels (25 total) sampled at 250 Hz with minimal preprocessing (bandpass filtering 0.05-200 Hz), making it a standard resource for evaluating multi-class motor imagery classification algorithms and cross-session transfer learning approaches.\",\n  \"methods_description\": \"EEG data were acquired using a BrainAmp MR plus amplifier with 22 EEG channels and 3 EOG channels sampled at 1000 Hz. Subjects performed cued motor imagery tasks lasting 4 seconds each, with visual and auditory stimulus presentation. Online filtering included bandpass 0.05-200 Hz and 50 Hz notch filtering. Data were recorded using BCI2000 software with a custom montage referenced to the left mastoid. The dataset is provided in two versions: original at 1000 Hz and downsampled to 100 Hz using a Chebyshev Type II filter (order 10, 50 dB stop band ripple).\",\n  \"license\": \"CC-BY-ND-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Michael Tangermann\": {},\n    \"Klaus-Robert Müller\": {},\n    \"Ad Aertsen\": {},\n    \"Niels Birbaumer\": {},\n    \"Christoph Braun\": {},\n    \"Clemens Brunner\": {\n      \"orcid\": \"0000-0002-6030-2233\"\n    },\n    \"Robert Leeb\": {},\n    \"Carsten Mehring\": {},\n    \"Kai J. Miller\": {},\n    \"Gernot R. Müller-Putz\": {},\n    \"Guido Nolte\": {},\n    \"Gert Pfurtscheller\": {},\n    \"Hubert Preissl\": {},\n    \"Gerwin Schalk\": {},\n    \"Alois Schlögl\": {},\n    \"Carmen Vidaurre\": {},\n    \"Stephan Waldert\": {},\n    \"Benjamin Blankertz\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"motor imagery\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"classification\"\n    },\n    {\n      \"term\": \"benchmark dataset\"\n    },\n    {\n      \"term\": \"multi-class motor imagery\"\n    },\n    {\n      \"term\": \"BCI competition\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.3389/fnins.2012.00055\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000139\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"References\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000139\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"EEG Dataset\",\n  \"modalities\": [\n    \"eeg\"\n  ],\n  \"sizes\": [\n    \"1.5 GB (129 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".html\",\n    \".json\",\n    \".mat\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"b7623f119ff818fd2933d86dc7ed769134b260b260500ef8eefce2247aee7211\"\n}","last_activity_at":"2026-08-16 13:26:41","source":null,"source_id":null,"subject_count":9,"modalities":"eeg","age_min":17,"age_max":26,"file_size":1485047253,"total_files":769,"tasks":"imagery","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Michael Tangermann, Klaus-Robert Müller, Ad Aertsen, Niels Birbaumer, Christoph Braun, Clemens Brunner, Robert Leeb, Carsten Mehring, Kai J. 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Müller-Putz, Guido Nolte, Gert Pfurtscheller, Hubert Preissl, Gerwin Schalk, Alois Schlögl, Carmen Vidaurre, Stephan Waldert, Benjamin Blankertz","license":"CC-BY-ND-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000139-blue)](https://doi.org/10.82901/nemar.nm000139)\n\n# BNCI 2014-001 Motor Imagery dataset\n\nBNCI 2014-001 Motor Imagery dataset.\n\n## Dataset Overview\n\n- **Code**: BNCI2014-001\n- **Paradigm**: imagery\n- **DOI**: 10.3389/fnins.2012.00055\n- **Subjects**: 9\n- **Sessions per subject**: 2\n- **Events**: left_hand=1, right_hand=2, feet=3, tongue=4\n- **Trial interval**: [2, 6] s\n- **Runs per session**: 6\n- **File format**: GDF\n- **Data preprocessed**: True\n\n## Acquisition\n\n- **Sampling rate**: 250.0 Hz\n- **Number of channels**: 25\n- **Channel types**: eeg=22, eog=3\n- **Channel names**: C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CPz, Cz, EOG1, EOG2, EOG3, FC1, FC2, FC3, FC4, FCz, Fz, P1, P2, POz, Pz\n- **Montage**: custom\n- **Hardware**: BrainAmp MR plus\n- **Software**: BCI2000\n- **Reference**: left mastoid\n- **Ground**: unknown\n- **Sensor type**: Ag/AgCl\n- **Line frequency**: 50.0 Hz\n- **Online filters**: bandpass 0.05-200 Hz\n- **Cap manufacturer**: EASYCAP GmbH\n\n## Participants\n\n- **Number of subjects**: 9\n- **Health status**: healthy\n- **Species**: human\n\n## Experimental Protocol\n\n- **Paradigm**: imagery\n- **Number of classes**: 4\n- **Class labels**: left_hand, right_hand, feet, tongue\n- **Trial duration**: 4.0 s\n- **Study design**: Two-class motor imagery (selected from left hand, right hand, and foot) with asynchronous/continuous control periods\n- **Feedback type**: none\n- **Stimulus type**: arrow_cue\n- **Stimulus modalities**: visual, auditory\n- **Primary modality**: multisensory\n- **Synchronicity**: asynchronous\n- **Mode**: offline\n- **Instructions**: Subjects instructed to perform motor imagery during cued periods\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n```\n  left_hand\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Leftward, Arrow\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Left, Hand\n\n  right_hand\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Rightward, Arrow\n    └─ Agent-action\n       └─ Imagine\n          ├─ Move\n          └─ Right, Hand\n\n  feet\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Downward, Arrow\n    └─ Agent-action\n       └─ Imagine, Move, Foot\n\n  tongue\n    ├─ Sensory-event\n    │  ├─ Experimental-stimulus\n    │  ├─ Visual-presentation\n    │  └─ Upward, Arrow\n    └─ Agent-action\n       └─ Imagine, Move, Tongue\n\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: motor_imagery\n- **Imagery tasks**: left_hand, right_hand, foot\n- **Cue duration**: 4.0 s\n- **Imagery duration**: 4.0 s\n\n## Data Structure\n\n- **Trials**: {'training': 200, 'test': 240}\n- **Blocks per session**: 6\n- **Trials context**: per subject (2 training runs + 4 test runs)\n\n## Preprocessing\n\n- **Data state**: minimally preprocessed (bandpass and notch filtered)\n- **Preprocessing applied**: True\n- **Steps**: bandpass filtering\n- **Highpass filter**: 0.05 Hz\n- **Lowpass filter**: 200 Hz\n- **Bandpass filter**: {'low_cutoff_hz': 0.05, 'high_cutoff_hz': 200.0}\n- **Filter type**: analog\n- **Re-reference**: none\n- **Downsampled to**: 100.0 Hz\n- **Notes**: Data provided in two versions: original at 1000 Hz and downsampled to 100 Hz (with Chebyshev Type II filter order 10, stop band ripple 50 dB, stop band edge 49 Hz)\n\n## Signal Processing\n\n- **Classifiers**: LDA, SVM, Neural Network, Naive Bayes, RBF Neural Network\n- **Feature extraction**: CSP, FBCSP, Bandpower, ERD, ERS\n- **Frequency bands**: mu=[8, 12] Hz; beta=[16, 24] Hz\n\n## Cross-Validation\n\n- **Method**: train-test split\n- **Evaluation type**: within_session\n\n## Performance (Original Study)\n\n- **Mse**: 0.382\n\n## BCI Application\n\n- **Applications**: cursor_control, communication\n- **Environment**: laboratory\n- **Online feedback**: False\n\n## Tags\n\n- **Pathology**: Healthy\n- **Modality**: Motor\n- **Type**: Motor\n\n## Documentation\n\n- **Description**: Review of the BCI competition IV - Data set 1: Asynchronous Motor Imagery\n- **DOI**: 10.3389/fnins.2012.00055\n- **License**: CC-BY-ND-4.0\n- **Investigators**: Michael Tangermann, Klaus-Robert Müller, Ad Aertsen, Niels Birbaumer, Christoph Braun, Clemens Brunner, Robert Leeb, Carsten Mehring, Kai J. Miller, Gernot R. Müller-Putz, Guido Nolte, Gert Pfurtscheller, Hubert Preissl, Gerwin Schalk, Alois Schlögl, Carmen Vidaurre, Stephan Waldert, Benjamin Blankertz\n- **Senior author**: Michael Tangermann\n- **Contact**: michael.tangermann@tu-berlin.de\n- **Institution**: Berlin Institute of Technology\n- **Department**: Machine Learning Laboratory\n- **Address**: FR 6-9, Franklinstr. 28/29, 10587 Berlin, Germany\n- **Country**: AT\n- **Repository**: BNCI Horizon\n- **Data URL**: http://www.bbci.de/competition/iv/\n- **Publication year**: 2012\n- **Keywords**: brain-computer interface, BCI, competition\n\n## References\n\nTangermann, M., Muller, K.R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G. and Nolte, G., 2012. Review of the BCI competition IV. Frontiers in neuroscience, 6, p.55.\n\nNotes\n\n.. note::\n\n``BNCI2014_001`` was previously named ``BNCI2014001``. ``BNCI2014001`` will be removed in version 1.1.\n\n.. versionadded:: 0.4.0\n\nThis is one of the most widely used motor imagery datasets in BCI research, commonly referred to as \"BCI Competition IV Dataset 2a\". It serves as a standard benchmark for 4-class motor imagery classification algorithms.\n\nThe dataset is particularly useful for:\n\n- Multi-class motor imagery classification (4 classes) - Transfer learning studies (9 subjects, 2 sessions each) - Cross-session variability analysis\n\nSee Also\n\nBNCI2014_004 : BCI Competition 2008 2-class motor imagery (Dataset B) BNCI2003_004 : BCI Competition III 2-class motor imagery\n\nExamples\n\n>>> from moabb.datasets import BNCI2014_001 >>> dataset = BNCI2014_001() >>> dataset.subject_list [1, 2, 3, 4, 5, 6, 7, 8, 9]\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n\nPernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. 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