{"dataset":{"id":"218","dataset_id":"nm000186","name":"BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects)","description":"This dataset comprises EEG recordings from 8 healthy subjects performing a P300-based brain-computer interface task using a 6x6 checkerboard visual stimulus paradigm. Recorded at 256 Hz with 16 channels using g.USBamp hardware, the data includes target and non-target event classifications for speller application development. This derivative dataset is part of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across multiple stimulus paradigms.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000186","concept_doi":"10.82901/nemar.nm000186","latest_version_doi":"10.82901/nemar.nm000186.v1.0.2","created_at":"2026-03-23 20:49:03","updated_at":"2026-08-18 20:38:53","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 E — 6x6 checkerboard (8 healthy subjects)\",\n  \"description\": \"This dataset comprises EEG recordings from 8 healthy subjects performing a P300-based brain-computer interface task using a 6x6 checkerboard visual stimulus paradigm. Recorded at 256 Hz with 16 channels using g.USBamp hardware, the data includes target and non-target event classifications for speller application development. This derivative dataset is part of the BigP3BCI study, the largest public P300 BCI dataset containing recordings from approximately 267 subjects across multiple stimulus paradigms.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz sampling rate using 16 channels arranged in a standard 10-20 montage with g.USBamp (g.tec) hardware. Line frequency was 60 Hz. Subjects performed a P300 speller task with a 6x6 checkerboard visual stimulus paradigm. Each trial had a duration of 1.0 second with target and non-target event classifications. Data were processed using MOABB (Mother of All BCI Benchmarks) with P300 ERP detection feature extraction and within-subject calibration-then-test cross-validation.\",\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\": \"E1A-Associated p300 Protein\",\n      \"subject_scheme\": \"MeSH\",\n      \"scheme_uri\": \"https://id.nlm.nih.gov/mesh/\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D050881\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\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      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D014796\"\n    },\n    {\n      \"term\": \"speller paradigm\"\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/nm000186\",\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/nm000186\",\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    \"541.5 MB (177 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"09b3d448b9139f77a1afca1476d8b1da0f964f4542aec6881f0f0f6fcacd25fd\"\n}","last_activity_at":"2026-08-11 15:15:54","source":null,"source_id":null,"subject_count":8,"modalities":"eeg","age_min":0,"age_max":0,"file_size":543136535,"total_files":667,"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.nm000186-blue)](https://doi.org/10.82901/nemar.nm000186)\n\n# BigP3BCI Study E — 6x6 checkerboard (8 healthy subjects)\n\nBigP3BCI Study E — 6x6 checkerboard (8 healthy subjects).\n\n## Dataset Overview\n\n- **Code**: Mainsah2025-E\n- **Paradigm**: p300\n- **DOI**: 10.13026/0byy-ry86\n- **Subjects**: 8\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**: 8\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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