{"dataset":{"id":"310","dataset_id":"nm000277","name":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study G)","description":"BigP3BCI Study G is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 checkerboard visual speller task. This derivative dataset is one of 20 studies in the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects total. The data were acquired at 256 Hz using 16-channel EEG with a g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000277","concept_doi":"10.82901/nemar.nm000277","latest_version_doi":"10.82901/nemar.nm000277.v1.0.3","created_at":"2026-03-27 12:41:28","updated_at":"2026-08-18 18:20:38","zenodo_concept_id":"20524317","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 G)\",\n  \"description\": \"BigP3BCI Study G is a P300-based brain-computer interface dataset comprising EEG recordings from 20 healthy subjects performing a 9x8 checkerboard visual speller task. This derivative dataset is one of 20 studies in the larger BigP3BCI collection, the largest public P300 BCI dataset with ~267 subjects total. The data were acquired at 256 Hz using 16-channel EEG with a g.USBamp amplifier and include target and non-target event classifications suitable for machine learning applications.\",\n  \"methods_description\": \"EEG data were acquired at 256 Hz sampling rate using 16 channels with standard 10-20 montage on a g.USBamp (g.tec) amplifier with 60 Hz line frequency. Subjects performed a visual P300 speller task using a 9x8 checkerboard paradigm with dynamic stimulus presentation. Each subject completed one session with target and non-target event classifications. Data were processed using MOABB (Mother of All BCI Benchmarks) with P300 ERP feature extraction and within-subject cross-validation using a calibration-then-test approach.\",\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\": \"P300 event-related potential\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"EEG\"\n    },\n    {\n      \"term\": \"visual evoked potentials\"\n    },\n    {\n      \"term\": \"event-related potential\"\n    },\n    {\n      \"term\": \"ERP\"\n    },\n    {\n      \"term\": \"speller paradigm\"\n    },\n    {\n      \"term\": \"checkerboard paradigm\"\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/nm000277\",\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/nm000277\",\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 (641 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"source_hash\": \"c8f55f249c282b00e5d1211be7458d20c42e538b93a85e6015425c5d73be3757\"\n}","last_activity_at":"2026-08-16 13:38:44","source":null,"source_id":null,"subject_count":20,"modalities":"eeg","age_min":0,"age_max":0,"file_size":1737377722,"total_files":2351,"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.nm000277-blue)](https://doi.org/10.82901/nemar.nm000277)\n\nMainsah2025-G\n=============\n\nBigP3BCI Study G — 9x8 checkerboard/dynamic (20 healthy subjects).\n\nDataset Overview\n----------------\n  Code: Mainsah2025-G\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 20\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: 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: 20\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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