{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd6","dataset_id":"nm000321","associated_paper_doi":null,"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":36,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/nm000321","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"0513bdc95abc065e1012284f261d2647cd8b00e54e1f76ea9ed2acf8d1212d4a","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 Q)","readme":"Mainsah2025-Q\n=============\nBigP3BCI Study Q — 6x6 color intensification (36 ALS subjects).\nDataset Overview\n----------------\n  Code: Mainsah2025-Q\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 36\n  Sessions per subject: 3\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: 36\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":1188983717,"source":"nemar","storage":{"backend":"s3","base":"s3://openneuro.org/nm000321","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-22T12:52:25.170173+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":360,"computed_title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study Q)","nchans_counts":[{"val":32,"count":360}],"sfreq_counts":[{"val":256.0000930697907,"count":208},{"val":256.00008203487505,"count":52},{"val":256.0,"count":43},{"val":256.00010076264726,"count":16},{"val":256.0001098418278,"count":12},{"val":256.00012071918457,"count":12},{"val":256.0001184842897,"count":7},{"val":256.00008886963377,"count":4},{"val":256.00010663894057,"count":3},{"val":256.00009694678226,"count":3}],"stats_computed_at":"2026-04-22T23:16:00.314528+00:00","total_duration_s":47302.57794447626,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"a3ed6d83a3dfa44e","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Other"],"modality":["Visual"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.7,"modality":0.9,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot conventions are the oddball-style datasets, because a P300 speller is fundamentally a target vs non-target ERP paradigm. For example, the few-shot \"Cross-modal Oddball Task\" (Parkinson’s) uses an oddball design and is labeled with Type=\"Clinical/Intervention\" because a clinical population is central and the work is framed around PD-related dysfunction/biomarkers. Another related convention is that when the dataset is primarily an applied/clinical cohort resource (vs basic cognition), Type tends to map to \"Clinical/Intervention\" rather than purely cognitive labels (e.g., the dementia resting-state dataset is also Type=\"Clinical/Intervention\"). This guides treating an ALS P300 speller dataset as a clinical/assistive-BCI dataset rather than only an \"Attention\"/\"Perception\" study.","metadata_analysis":"Key quoted metadata facts:\n1) Population: \"BigP3BCI Study Q — 6x6 color intensification (36 ALS subjects).\" (explicit ALS clinical population)\n2) Conflicting line: \"Health status: healthy\" (participant section)\n3) Stimulus modality: HED annotations include \"Visual-presentation\" for both Target and NonTarget, and also \"Tags ... Modality: visual\".\n4) Task/application framing: \"Paradigm: p300\" and \"BCI Application ... Applications: speller\" with \"Online feedback: True\".\n5) ERP/BCI focus: \"Feature extraction: P300_ERP_detection\" and \"Events: Target=2, NonTarget=1\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"(36 ALS subjects)\" but also says \"Health status: healthy\".\n- Few-shot pattern suggests: when an explicit clinical group is recruited, do not label Healthy.\n- Alignment status: CONFLICT within metadata (ALS vs healthy). Resolution: the explicit clinical-population fact (ALS) takes precedence over the contradictory \"healthy\" field.\n\nModality:\n- Metadata says: \"6x6 color intensification\" and HED includes \"Visual-presentation\"; also \"Modality: visual\".\n- Few-shot pattern suggests: label modality by stimulus channel; oddball/speller intensifications are Visual.\n- Alignment status: ALIGN.\n\nType:\n- Metadata says: \"Paradigm: p300\", \"BCI Application... speller\", \"Online feedback: True\", and \"P300_ERP_detection\".\n- Few-shot pattern suggests: oddball/P300 paradigms can be attention-related, but when the dataset is clearly a clinical cohort resource and BCI application is central, Type often maps to \"Clinical/Intervention\".\n- Alignment status: PARTIAL ALIGN (task mechanics are cognitive/ERP, but applied clinical BCI framing supports Clinical/Intervention).","decision_summary":"Top-2 candidate labels (head-to-head):\n\nPathology:\n1) Other — Evidence: explicit clinical group \"(36 ALS subjects)\". ALS is not an allowed pathology label, so it maps to \"Other\".\n2) Healthy — Evidence: contradictory line \"Health status: healthy\".\nDecision: Other wins because explicit recruitment population (ALS) is a higher-priority fact than an inconsistent health-status field. (CONFLICT resolved in favor of explicit clinical population.)\n\nModality:\n1) Visual — Evidence: \"6x6 color intensification\", HED \"Visual-presentation\", and \"Modality: visual\".\n2) Multisensory — Little/no evidence of auditory/tactile components.\nDecision: Visual.\n\nType:\n1) Clinical/Intervention — Evidence: clinical end-user population \"ALS subjects\" + explicit applied context \"BCI Application... speller\" and \"Online feedback: True\".\n2) Attention — Evidence: P300 target vs non-target paradigm (\"Events: Target... NonTarget\", \"Paradigm: p300\") implies selective attention/oddball processing.\nDecision: Clinical/Intervention, because the dataset is framed as an assistive/clinical BCI speller dataset in a clinical population, not purely a basic attention experiment.\n\nConfidence justification:\n- Pathology=0.7: one strong explicit quote for ALS plus an explicit contradictory \"healthy\" line reduces certainty.\n- Modality=0.9: 3+ direct supporting quotes/features (color intensification, HED Visual-presentation, Modality: visual) with no real competitor.\n- Type=0.7: multiple applied/clinical-BCI quotes (speller application, online feedback, ALS cohort) but no single explicit sentence saying \"clinical intervention\", leaving some ambiguity vs Attention."}},"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_Q"}}