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Trial intervals span 0-1.0 seconds from stimulus onset.","owner_user_id":19,"status":"active","github_repo":"nemarDatasets/nm000313","concept_doi":"10.82901/nemar.nm000313","latest_version_doi":"10.82901/nemar.nm000313.v1.0.3","created_at":"2026-03-28 02:49:12","updated_at":"2026-08-18 18:21:05","zenodo_concept_id":"20525111","is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study S2)\",\n  \"description\": \"BigP3BCI Study S2 is a P300-based brain-computer interface dataset comprising EEG recordings from 24 healthy subjects performing a 9x8 house/tool visual speller paradigm. The dataset contains 32-channel EEG data sampled at 256 Hz, annotated with target and non-target event classifications using HED 8.4.0 schema. This dataset is part of the larger BigP3BCI collection (the largest public P300 BCI dataset with recordings from approximately 267 subjects across 20 studies), designed to support machine learning research and BCI benchmarking. Trial intervals span 0-1.0 seconds from stimulus onset.\",\n  \"methods_description\": \"EEG data were acquired using a g.USBamp amplifier (g.tec) with 32 channels arranged in a standard 10-20 montage at a sampling rate of 256 Hz with 60 Hz line frequency filtering. Subjects performed a visual P300 speller task using a 9x8 character grid paradigm with target and non-target stimulus presentations. Each subject completed one session with analysis windows of 0-1.0 seconds post-stimulus. Event annotations follow HED 8.4.0 schema with Target and NonTarget classifications.\",\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\": \"P300\"\n    },\n    {\n      \"term\": \"brain-computer interface\"\n    },\n    {\n      \"term\": \"visual speller\"\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\": \"machine learning\"\n    },\n    {\n      \"term\": \"BCI benchmark\"\n    },\n    {\n      \"term\": \"classification\"\n    },\n    {\n      \"term\": \"P300 classification\"\n    },\n    {\n      \"term\": \"HED annotation\"\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/nm000313\",\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/nm000313\",\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    \"4.0 GB (577 files)\"\n  ],\n  \"formats\": [\n    \".bdf\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yaml\",\n    \".yml\"\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"National Institutes of Health\",\n      \"award_number\": \"Not specified\"\n    }\n  ],\n  \"source_hash\": \"76e551df6d9f6c317f58ddd3672d5f4517f72141f68b7a20077d8c3fd00e5953\"\n}","last_activity_at":"2026-08-16 13:39:05","source":null,"source_id":null,"subject_count":24,"modalities":"eeg","age_min":0,"age_max":0,"file_size":4018255723,"total_files":2147,"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.nm000313-blue)](https://doi.org/10.82901/nemar.nm000313)\n\nMainsah2025-S2\n==============\n\nBigP3BCI Study S2 — 9x8 house/tool paradigm (24 healthy subjects).\n\nDataset Overview\n----------------\n  Code: Mainsah2025-S2\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 24\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: 32\n  Channel types: eeg=32\n  Montage: standard_1020\n  Hardware: g.USBamp (g.tec)\n  Line frequency: 60.0 Hz\n\nParticipants\n------------\n  Number of subjects: 24\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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