{"dataset":{"id":"334","dataset_id":"nm000301","name":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study D)","description":"BigP3BCI Study D is a P300-based brain-computer interface dataset comprising EEG recordings from 17 healthy subjects performing a 6x6 character grid speller task. The dataset contains single-session recordings acquired at 256 Hz using 32-channel EEG with standard 10-20 montage, annotated with target and non-target event labels. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is formatted according to BIDS standards with HED event annotations for machine learning applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000301","concept_doi":"10.82901/nemar.nm000301","latest_version_doi":"10.82901/nemar.nm000301.v1.0.3","created_at":"2026-03-27 21:58:51","updated_at":"2026-08-18 18:22:01","zenodo_concept_id":"20524569","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study D)\",\n  \"description\": \"BigP3BCI Study D is a P300-based brain-computer interface dataset comprising EEG recordings from 17 healthy subjects performing a 6x6 character grid speller task. The dataset contains single-session recordings acquired at 256 Hz using 32-channel EEG with standard 10-20 montage, annotated with target and non-target event labels. This derivative dataset is part of the larger BigP3BCI collection, the largest public P300 BCI dataset, and is formatted according to BIDS standards with HED event annotations for machine learning applications.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz sampling rate using a g.USBamp amplifier (g.tec) with 32 channels arranged in standard 10-20 montage. Subjects performed a P300 speller task using a 6x6 dynamic row-column paradigm. Line frequency was 60 Hz. Single session per subject with target and non-target event classification.\",\n  \"license\": \"CC-BY-4.0\",\n  \"dataset_type\": \"derivative\",\n  \"authors\": {\n    \"Boyla Mainsah\": {},\n    \"Chance Fleeting\": {\n      \"orcid\": \"0000-0002-6271-4952\",\n      \"affiliations\": [\n        {\n          \"name\": \"Duke University\"\n        }\n      ]\n    },\n    \"Thomas Balmat\": {},\n    \"Eric Sellers\": {},\n    \"Leslie Collins\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Evoked Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018981\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"event-related potential\"\n    },\n    {\n      \"term\": \"speller paradigm\"\n    },\n    {\n      \"term\": \"visual perception\"\n    },\n    {\n      \"term\": \"machine learning\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"10.13026/0byy-ry86\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/nm000301\",\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/nm000301\",\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    \"2.6 GB (615 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"a18bb56cd8ff076bc4ecfbbeb5e6e4e0a548f6edf6cc199db0df55dd256cb20c\"\n}","last_activity_at":"2026-08-16 13:38:47","source":null,"source_id":null,"subject_count":17,"modalities":"eeg","age_min":0,"age_max":0,"file_size":2591517126,"total_files":2245,"tasks":"p300","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins","license":"CC-BY-4.0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000301-blue)](https://doi.org/10.82901/nemar.nm000301)\n\nMainsah2025-D\n=============\n\nBigP3BCI Study D — 6x6 dynamic/row-column (17 healthy subjects).\n\nDataset Overview\n----------------\n  Code: Mainsah2025-D\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 17\n  Sessions per subject: 1\n  Events: Target=2, NonTarget=1\n  Trial interval: [0, 1.0] s\n\nAcquisition\n-----------\n  Sampling rate: 256.0 Hz\n  Number of channels: 32\n  Channel types: eeg=32\n  Montage: standard_1020\n  Hardware: g.USBamp (g.tec)\n  Line frequency: 60.0 Hz\n\nParticipants\n------------\n  Number of subjects: 17\n  Health status: healthy\n\nExperimental Protocol\n---------------------\n  Paradigm: p300\n  Number of classes: 2\n  Class labels: Target, NonTarget\n\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\n\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: p300\n\nSignal Processing\n-----------------\n  Feature extraction: P300_ERP_detection\n\nCross-Validation\n----------------\n  Method: calibration-then-test\n  Evaluation type: within_subject\n\nBCI Application\n---------------\n  Applications: speller\n  Environment: laboratory\n  Online feedback: True\n\nTags\n----\n  Modality: visual\n  Type: perception\n\nDocumentation\n-------------\n  Description: BigP3BCI: the largest public P300 BCI dataset, containing EEG recordings from ~267 subjects across 20 studies using 6x6 or 9x8 character grids with various stimulus paradigms.\n  DOI: 10.13026/0byy-ry86\n  License: CC-BY-4.0\n  Investigators: Boyla Mainsah, Chance Fleeting, Thomas Balmat, Eric Sellers, Leslie Collins\n  Institution: Duke University; East Tennessee State University\n  Country: US\n  Repository: PhysioNet\n  Data URL: https://physionet.org/content/bigp3bci/1.0.0/\n  Publication year: 2025\n\nReferences\n----------\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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