{"success":true,"database":"eegdash","data":{"_id":"69d16e06897a7725c66f4ce7","dataset_id":"nm000351","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":"2125f64abf998e5bc5ea3532e4d50a52a6601a11c3f85bfe5506a5a97e435cd4","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 P)","readme":"Mainsah2025-P\n=============\nBigP3BCI Study P — 9x8 predictive/non-predictive spelling (19 ALS subjects).\nDataset Overview\n----------------\n  Code: Mainsah2025-P\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 19\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: 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","1"],"size_bytes":1596681321,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000351","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:57.575484+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:02:01Z"},"total_files":228,"computed_title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study P)","nchans_counts":[{"val":32,"count":228}],"sfreq_counts":[{"val":256.0000930697907,"count":152},{"val":256.00008203487505,"count":38},{"val":256.0,"count":22},{"val":256.0000896869156,"count":1},{"val":256.0001141647201,"count":1},{"val":256.0001196052653,"count":1},{"val":256.0000983717175,"count":1},{"val":256.00009240629606,"count":1},{"val":256.00011666197287,"count":1},{"val":256.00008676866054,"count":1},{"val":256.0001109005733,"count":1},{"val":256.00007259581656,"count":1},{"val":256.0000936228914,"count":1},{"val":256.00009044741086,"count":1},{"val":256.0001237687188,"count":1},{"val":256.00011860780387,"count":1},{"val":256.00006037203326,"count":1},{"val":256.00009466146327,"count":1},{"val":256.00008694547637,"count":1}],"stats_computed_at":"2026-05-01T13:49:34.659873+00:00","total_duration_s":63669.09190617447,"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":["Perception"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"The Parkinson's cross-modal oddball example maps target-related oddball processing to Attention, making it the closest paradigm reference for P300 target/non-target events. The visual discrimination example maps visually presented target judgments to Perception. Neither example determines this dataset's population; its explicit ALS description takes precedence.","metadata_analysis":"The study-specific description says \"9x8 predictive/non-predictive spelling (19 ALS subjects),\" but a later, apparently generic field says \"Health status: healthy.\" Stimulus annotations include \"Visual-presentation\" for both Target and NonTarget, and the tags say \"Modality: visual\" and \"Type: perception.\" The protocol identifies a \"P300_ERP_detection\" feature and an \"Applications: speller\" BCI application.","paper_abstract_analysis":"The abstract explicitly studies P300 BCI performance \"in individuals with amyotrophic lateral sclerosis (ALS)\" and compares models trained on \"ALS data\" with those trained on \"non-ALS data.\" This supports ALS recruitment and a BCI-performance aim, not diagnosis or treatment.","evidence_alignment_check":"Pathology: Metadata says \"19 ALS subjects\" but also \"Health status: healthy.\" Few-shot patient-versus-control examples suggest labeling the recruited patient condition; this aligns with the study-specific description and abstract but conflicts with the generic health-status field. The specific ALS evidence wins. Modality: Metadata says \"Visual-presentation\" and \"Modality: visual\"; the visual-task examples align, with no conflict. Type: Metadata says \"Type: perception\" and describes Target/NonTarget P300 spelling. The oddball example suggests Attention, while the visual discrimination example supports Perception. These conventions differ; the explicit type tag and target discrimination favor Perception. No metadata fact is overridden by a few-shot pattern.","decision_summary":"Pathology candidates: ALS is supported by \"19 ALS subjects\" and the abstract's \"individuals with amyotrophic lateral sclerosis (ALS)\"; Healthy is supported by \"Health status: healthy.\" ALS wins because the study-specific population description and paper outweigh that conflicting field (confidence 0.8). Modality candidates: Visual is supported by \"Visual-presentation\" for both event classes and \"Modality: visual\"; Multisensory has no documented second stimulus channel. Visual wins (0.9). Type candidates: Perception is supported by \"Type: perception\" and Target/NonTarget visual-stimulus classification; Attention is supported by the P300 oddball-like paradigm and its few-shot analog. Perception wins on the explicit metadata tag, although Attention remains plausible (0.8)."}},"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_P","associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}