{"dataset":{"id":"429","dataset_id":"nm000157","name":"BigP3BCI Study B — 6x6 checkerboard, multi-session (19 healthy subjects)","description":"BigP3BCI Study B is a derivative EEG dataset containing P300 brain-computer interface recordings from 19 ALS patients across 8 sessions each, using a 6x6 checkerboard visual speller paradigm. The dataset comprises 16-channel EEG data sampled at 256 Hz, annotated with target and non-target event classifications using HED 8.4.0 schema. This dataset is a subset of the larger BigP3BCI collection, the largest public P300 BCI dataset with recordings from approximately 267 subjects across multiple studies.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000157","concept_doi":"10.82901/nemar.nm000157","latest_version_doi":"10.82901/nemar.nm000157.v1.0.2","created_at":"2026-04-14 12:28:50","updated_at":"2026-08-18 18:25:47","zenodo_concept_id":"20482064","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"BigP3BCI Study B — 6x6 checkerboard, multi-session (19 healthy subjects)\",\n  \"description\": \"BigP3BCI Study B is a derivative EEG dataset containing P300 brain-computer interface recordings from 19 ALS patients across 8 sessions each, using a 6x6 checkerboard visual speller paradigm. The dataset comprises 16-channel EEG data sampled at 256 Hz, annotated with target and non-target event classifications using HED 8.4.0 schema. This dataset is a subset of the larger BigP3BCI collection, the largest public P300 BCI dataset with 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 in standard 10-20 montage at 256 Hz sampling rate. The experimental paradigm employed a 6x6 checkerboard visual speller with target and non-target stimulus presentations. Data were processed using the MOABB (Mother of All BCI Benchmarks) framework version 1.5.0 with P300 ERP feature extraction. Evaluation employed a calibration-then-test cross-validation method with within-subject assessment.\",\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\": \"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\": \"Brain-Computer Interfaces\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D062207\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"visual perception\"\n    },\n    {\n      \"term\": \"speller paradigm\"\n    },\n    {\n      \"term\": \"amyotrophic lateral sclerosis\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D000274\"\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/nm000157\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000157\",\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    \"4.6 GB (1089 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"82ed6fed117b006232b630cb926b689c131aefdb64cc3ca5573604377e84a44d\"\n}","last_activity_at":"2026-08-16 14:08:23","source":null,"source_id":null,"subject_count":19,"modalities":"eeg","age_min":0,"age_max":0,"file_size":4598129361,"total_files":4119,"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.nm000157-blue)](https://doi.org/10.82901/nemar.nm000157)\n\n# BigP3BCI Study B — 6x6 checkerboard, multi-session (19 healthy subjects).\n\nDataset Overview\n----------------\n  Code: Mainsah2025-B\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 19\n  Sessions per subject: 8\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: 16\n  Channel types: eeg=16\n  Montage: standard_1020\n  Hardware: g.USBamp (g.tec)\n  Line frequency: 60.0 Hz\n\nParticipants\n------------\n  Number of subjects: 19\n  Health status: patients\n  Clinical population: ALS\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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