{"dataset":{"id":"233","dataset_id":"nm000200","name":"BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)","description":"This dataset comprises EEG recordings from 13 healthy subjects performing a P300-based brain-computer interface task using a 9x8 checkerboard stimulus paradigm. Participants completed a single session of visual perception tasks designed for speller applications, with 16-channel EEG data sampled at 256 Hz. This derivative dataset is part of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across multiple studies.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000200","concept_doi":"10.82901/nemar.nm000200","latest_version_doi":"10.82901/nemar.nm000200.v1.0.2","created_at":"2026-03-24 01:30:50","updated_at":"2026-08-18 21:11:41","zenodo_concept_id":"20501080","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)\",\n  \"description\": \"This dataset comprises EEG recordings from 13 healthy subjects performing a P300-based brain-computer interface task using a 9x8 checkerboard stimulus paradigm. Participants completed a single session of visual perception tasks designed for speller applications, with 16-channel EEG data sampled at 256 Hz. This derivative dataset is part of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across multiple studies.\",\n  \"methods_description\": \"EEG data were acquired using a g.USBamp amplifier (g.tec) with 16 channels positioned according to the standard 10-20 montage. Sampling rate was 256 Hz with a line frequency of 60 Hz. The experimental paradigm employed a 9x8 checkerboard visual stimulus presentation with target and non-target event classifications. Trial intervals were defined from 0 to 1.0 seconds, and within-subject calibration-then-test cross-validation was applied for evaluation.\",\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\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"Event-Related Potentials, P300\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D018913\"\n    },\n    {\n      \"term\": \"Visual Perception\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D014796\"\n    },\n    {\n      \"term\": \"speller application\"\n    },\n    {\n      \"term\": \"checkerboard\"\n    },\n    {\n      \"term\": \"visual stimulus\"\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/nm000200\",\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/nm000200\",\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    \"1.7 GB (531 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"2e265957695b058d6b35fc098bef8fd139bc8728ee67b4e4b03657db4a40cbb7\"\n}","last_activity_at":"2026-08-16 13:35:02","source":null,"source_id":null,"subject_count":13,"modalities":"eeg","age_min":0,"age_max":21,"file_size":1683426167,"total_files":1931,"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.nm000200-blue)](https://doi.org/10.82901/nemar.nm000200)\n\n# BigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects)\n\nBigP3BCI Study I — 9x8 checkerboard/performance-based (13 healthy subjects).\n\n## Dataset Overview\n\n- **Code**: Mainsah2025-I\n- **Paradigm**: p300\n- **DOI**: 10.13026/0byy-ry86\n- **Subjects**: 13\n- **Sessions per subject**: 1\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 1.0] s\n\n## Acquisition\n\n- **Sampling rate**: 256.0 Hz\n- **Number of channels**: 16\n- **Channel types**: eeg=16\n- **Montage**: standard_1020\n- **Hardware**: g.USBamp (g.tec)\n- **Line frequency**: 60.0 Hz\n\n## Participants\n\n- **Number of subjects**: 13\n- **Health status**: healthy\n\n## Experimental Protocol\n\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n\n## HED Event Annotations\n\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n\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\n```\n## Paradigm-Specific Parameters\n\n- **Detected paradigm**: p300\n\n## Signal Processing\n\n- **Feature extraction**: P300_ERP_detection\n\n## Cross-Validation\n\n- **Method**: calibration-then-test\n- **Evaluation type**: within_subject\n\n## BCI Application\n\n- **Applications**: speller\n- **Environment**: laboratory\n- **Online feedback**: True\n\n## Tags\n\n- **Modality**: visual\n- **Type**: perception\n\n## Documentation\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\n## References\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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