{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cdb","dataset_id":"nm000336","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":20,"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":"e59f957273e9cbc1d2834d038a8306214c92f504d249b6385b7462eae7e4fbfc","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 R)","readme":"Mainsah2025-R\n=============\nBigP3BCI Study R — 9x8 multi-face paradigms (20 ALS subjects).\nDataset Overview\n----------------\n  Code: Mainsah2025-R\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 20\n  Sessions per subject: 2\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: 20\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","1"],"size_bytes":2117757234,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000336","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:54.790198+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:02:53Z"},"total_files":480,"computed_title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study R)","nchans_counts":[{"val":32,"count":480}],"sfreq_counts":[{"val":256.0000579103764,"count":249},{"val":256.00011324306917,"count":132},{"val":256.00009140820043,"count":39},{"val":256.0000766323896,"count":27},{"val":256.000065968772,"count":18},{"val":256.0,"count":15}],"stats_computed_at":"2026-05-01T13:49:34.659694+00:00","total_duration_s":84694.10175496984,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["ALS"],"modality":["Visual"],"type":["Clinical","Attention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"The Parkinson's cross-modal oddball example maps target-related attention in a clinical cohort to Clinical and Attention. That convention is more relevant to this P300 target/non-target paradigm than the visual discrimination example, which maps sensory discrimination to Perception. The examples do not determine this dataset's diagnosis.","metadata_analysis":"The study-specific description says \"BigP3BCI Study R — 9x8 multi-face paradigms (20 ALS subjects),\" but a later field says \"Health status: healthy.\" The specific Study R population statement is supported by the paper abstract. Stimulus annotations specify \"Visual-presentation\" for both Target and NonTarget, and the tags say \"Modality: visual.\" The protocol lists \"Class labels: Target, NonTarget,\" and the BCI application is \"speller.\" A generated tag says \"Type: perception.\"","paper_abstract_analysis":"The abstract explicitly studies P300 BCI performance \"in individuals with amyotrophic lateral sclerosis (ALS)\" and compares models trained on ALS and non-ALS data. This supports interpreting Study R as an ALS cohort despite the contradictory health-status field. It also supports a clinical BCI-performance aim.","evidence_alignment_check":"Pathology: Metadata says \"20 ALS subjects\" but also \"Health status: healthy.\" Clinical-cohort few-shot examples suggest labeling the recruited condition, not controls or a generic status field. These metadata fields conflict; the study-specific ALS statement wins because the abstract independently corroborates it. Modality: Metadata says \"Visual-presentation\" and \"Modality: visual\"; the visual-task examples suggest Visual. They align. Type: Metadata says \"Target, NonTarget\" and \"speller,\" while a generated tag says \"Type: perception.\" The clinical oddball example suggests Clinical plus Attention for target-related processing in patients. This conflicts with the broad perception tag; the described P300 paradigm and ALS BCI-performance aim support Clinical plus Attention more specifically.","decision_summary":"Pathology candidates: ALS, supported by \"20 ALS subjects\" and the corroborating abstract; Healthy, supported by \"Health status: healthy.\" Choose ALS, with reduced confidence (0.8) because the metadata contradicts itself. Modality candidates: Visual, supported by \"multi-face paradigms,\" \"Visual-presentation,\" and \"Modality: visual\"; Multisensory, with no second channel documented. Choose Visual (0.9). Type candidates: Clinical plus Attention, supported by the ALS-specific BCI aim, \"Target, NonTarget,\" \"Paradigm: p300,\" and the clinical oddball few-shot convention; Perception, supported by \"Type: perception.\" Choose Clinical plus Attention (0.8), since the tag creates some uncertainty. No metadata diagnosis was overridden by a few-shot pattern."}},"canonical_name":null,"name_confidence":0.72,"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_R","associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}