{"dataset":{"id":"208","dataset_id":"nm000176","name":"BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects)","description":"This dataset comprises EEG recordings from 5 healthy subjects performing a P300-based brain-computer interface (BCI) speller task using a 9x8 adaptive/checkerboard stimulus paradigm across 2 sessions. The data were acquired at 256 Hz using 16 EEG channels and represent a derivative subset of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across 20 studies. This dataset is suitable for P300 detection, feature extraction, and within-subject BCI performance evaluation.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000176","concept_doi":"10.82901/nemar.nm000176","latest_version_doi":"10.82901/nemar.nm000176.v1.0.3","created_at":"2026-03-23 14:56:04","updated_at":"2026-08-18 18:16:31","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects)\",\n  \"description\": \"This dataset comprises EEG recordings from 5 healthy subjects performing a P300-based brain-computer interface (BCI) speller task using a 9x8 adaptive/checkerboard stimulus paradigm across 2 sessions. The data were acquired at 256 Hz using 16 EEG channels and represent a derivative subset of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across 20 studies. This dataset is suitable for P300 detection, feature extraction, and within-subject BCI performance evaluation.\",\n  \"methods_description\": \"EEG data were acquired using a g.USBamp amplifier (g.tec) with 16 channels arranged in a standard 10-20 montage at a sampling rate of 256 Hz. The experimental paradigm employed a P300 speller task with a 9x8 character grid using adaptive/checkerboard stimulus presentation. Each trial had a duration of 1.0 second, with visual stimuli classified as Target or NonTarget events. Online feedback was provided to subjects during the laboratory-based sessions.\",\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\": \"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\": \"brain-computer interfaces\"\n    },\n    {\n      \"term\": \"speller paradigm\"\n    },\n    {\n      \"term\": \"adaptive 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/nm000176\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.21105/joss.01896\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-019-0104-8\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/nm000176\",\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    \"826.2 MB (260 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".html\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"5d42b10ca1b70b097573a6f839ba9ab5ef04900f18e4d5ee0db052c9078b164e\"\n}","last_activity_at":"2026-08-16 13:35:05","source":null,"source_id":null,"subject_count":5,"modalities":"eeg","age_min":23,"age_max":27,"file_size":828764840,"total_files":950,"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.nm000176-blue)](https://doi.org/10.82901/nemar.nm000176)\n\n# BigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects)\n\nBigP3BCI Study K — 9x8 adaptive/checkerboard, 2 sessions (5 healthy subjects).\n\n## Dataset Overview\n\n- **Code**: Mainsah2025-K\n- **Paradigm**: p300\n- **DOI**: 10.13026/0byy-ry86\n- **Subjects**: 5\n- **Sessions per subject**: 2\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**: 5\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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