{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd8","dataset_id":"nm000326","associated_paper_doi":"10.1609/aaai.v40i48.42219","authors":["Boyla Mainsah","Chance Fleeting","Thomas Balmat","Eric Sellers","Leslie Collins"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.13026/0byy-ry86","datatypes":["eeg"],"demographics":{"subjects_count":19,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://doi.org/10.1609/aaai.v40i48.42219"},"funding":[],"ingestion_fingerprint":"4a9d3d5da8d29477274c840fb596a0c146167a0b41a797fa0698462d9f7a79bc","license":"CC-BY-4.0","n_contributing_labs":null,"name":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study C)","readme":"Mainsah2025-C\n=============\nBigP3BCI Study C — 6x6 checkerboard with ERN (19 healthy subjects).\nDataset Overview\n----------------\n  Code: Mainsah2025-C\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 19\n  Sessions per subject: 1\n  Events: Target=2, NonTarget=1\n  Trial interval: [0, 1.0] s\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\nParticipants\n------------\n  Number of subjects: 19\n  Health status: healthy\nExperimental Protocol\n---------------------\n  Paradigm: p300\n  Number of classes: 2\n  Class labels: Target, NonTarget\nHED Event Annotations\n---------------------\n  Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n  Target\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Target\n  NonTarget\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Non-target\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: p300\nSignal Processing\n-----------------\n  Feature extraction: P300_ERP_detection\nCross-Validation\n----------------\n  Method: calibration-then-test\n  Evaluation type: within_subject\nBCI Application\n---------------\n  Applications: speller\n  Environment: laboratory\n  Online feedback: True\nTags\n----\n  Modality: visual\n  Type: perception\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\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\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. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\n---\nGenerated by MOABB 1.5.0 (Mother of All BCI Benchmarks)\nhttps://github.com/NeuroTechX/moabb","recording_modality":["eeg"],"senior_author":null,"sessions":["0"],"size_bytes":1321764478,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000326","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:54.362005+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:03:20Z"},"total_files":341,"computed_title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study C)","nchans_counts":[{"val":32,"count":341}],"sfreq_counts":[{"val":256.0000775610915,"count":284},{"val":256.0001218685495,"count":57}],"stats_computed_at":"2026-05-01T13:49:34.659665+00:00","total_duration_s":52843.65092272895,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"The visual discrimination example (Meta-rdk) maps visual target discrimination to Visual and Perception, despite requiring a response. The healthy motor-imagery BCI example shows that BCI technology alone does not determine Type: the construct studied does. The clinical oddball examples show when Attention and Clinical are appropriate, but their patient cohorts do not describe Study C.","metadata_analysis":"Study C is described as a \"6x6 checkerboard with ERN (19 healthy subjects)\" and lists \"Health status: healthy.\" Its Target and NonTarget events are annotated \"Visual-presentation,\" and its tags say \"Modality: visual\" and \"Type: perception.\" The protocol specifies \"Paradigm: p300\" and \"Class labels: Target, NonTarget\"; the listed BCI application is \"speller.\"","paper_abstract_analysis":"The abstract studies P300 BCI models in individuals with ALS and compares ALS with non-ALS training data. It does not identify the participants in this specific Study C cohort. Its broader clinical focus therefore does not override Study C's explicit healthy-subject metadata.","evidence_alignment_check":"Pathology: Metadata says \"19 healthy subjects\" and \"Health status: healthy\"; the healthy BCI example suggests Healthy. ALIGN. The abstract's ALS population concerns the broader analysis, not an explicit Study C recruitment fact. Modality: Metadata says \"Visual-presentation\" and \"Modality: visual\"; the visual discrimination and BCI examples suggest Visual. ALIGN. Type: Metadata says \"Type: perception\" and describes \"Target, NonTarget\" visual events; the visual discrimination example suggests Perception. ALIGN. Clinical oddball examples suggest Attention as a possibility for target processing, but they concern clinical cohorts and do not outweigh this dataset's explicit type tag.","decision_summary":"Pathology candidates: Healthy (\"19 healthy subjects\"; \"Health status: healthy\") versus ALS (broader paper abstract). Healthy wins because cohort-specific metadata takes precedence; confidence 0.9, supported by two direct quotes plus a clear healthy-BCI analog. Modality candidates: Visual (\"Visual-presentation\"; \"Modality: visual\"; \"6x6 checkerboard\") versus Multisensory (no second stimulus channel documented). Visual wins; confidence 0.9. Type candidates: Perception (\"Type: perception\"; visual \"Target, NonTarget\" discrimination) versus Attention (P300 target processing). Perception wins on the explicit type tag and discrimination convention; confidence 0.8. No cohort-specific metadata fact was overridden by a few-shot pattern."}},"canonical_name":null,"name_confidence":0.98,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Mainsah2025_C","associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}