{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd6","dataset_id":"nm000321","associated_paper_doi":"10.1088/1741-2552/aa7525","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":{"paper_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6038809"},"funding":[],"ingestion_fingerprint":"785b176d0ccd30997ea93e55f27f7207f6ba847e78fb94e1f4e4b4936ed06ca2","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":1189231524,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000321","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:53.257635+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:03:49Z"},"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":256.00009694678226,"count":3},{"val":256.00010663894057,"count":3}],"stats_computed_at":"2026-05-01T13:49:34.659631+00:00","total_duration_s":47302.57794447626,"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":["Attention","Clinical"],"confidence":{"pathology":0.7,"modality":0.9,"type":0.7},"reasoning":{"few_shot_analysis":"The Parkinson's cross-modal oddball example maps target-versus-standard stimuli in a clinical cohort to Attention and Clinical. Study Q similarly uses a P300 target/non-target paradigm. The visual discrimination example shows that Perception fits sensory discrimination, whereas this speller depends on attending to target events. These examples guide label conventions, not participant facts.","metadata_analysis":"The study-specific heading says \"6x6 color intensification (36 ALS subjects),\" but a later field says \"Health status: healthy\"; the population fields conflict. The protocol lists \"Class labels: Target, NonTarget,\" the event annotations specify \"Visual-presentation,\" and the application is \"speller.\" The generated tags say \"Modality: visual\" and \"Type: perception.\"","paper_abstract_analysis":"The abstract describes the P300 speller as detecting responses to a user \"attending to relevant or target stimulus events\" and studies online spelling performance. It supports an attention-based BCI interpretation but does not resolve the conflicting participant descriptions.","evidence_alignment_check":"Pathology: Metadata says \"36 ALS subjects\" and also \"Health status: healthy\"; the few-shot clinical-cohort pattern suggests labeling the recruited condition. CONFLICT within metadata: the study-specific ALS description takes precedence over the generic health-status field, so ALS wins. Modality: \"Visual-presentation\" and \"Modality: visual\" ALIGN with the visual-stimulus labeling convention. Type: \"Target, NonTarget\" and \"speller\" ALIGN with the oddball example's Attention convention. The generated \"Type: perception\" tag CONFLICTS with that interpretation; the task description and abstract's emphasis on attending to targets favor Attention. The clinical-cohort example also suggests Clinical, although the dataset does not explicitly state that ALS itself is the research outcome.","decision_summary":"Pathology top two: ALS, supported by \"36 ALS subjects,\" versus Healthy, supported by \"Health status: healthy.\" Select ALS because the study-specific recruitment statement is more informative; confidence 0.7 reflects the direct contradiction and single specific supporting quote. Modality top two: Visual, supported by \"6x6 color intensification,\" \"Visual-presentation,\" and \"Modality: visual,\" versus Multisensory, with no second stimulus channel documented. Select Visual, confidence 0.9. Type top two: Attention, supported by \"Target, NonTarget,\" \"speller,\" the P300 paradigm, and the oddball few-shot analogy, versus Perception, supported by the generated \"Type: perception\" tag. Select Attention; also include Clinical by analogy to clinical-cohort oddball datasets, but with less direct evidence that ALS is an outcome of interest. Type confidence is 0.7 because the purpose is partly inferred and the metadata tag disagrees. No study-specific population fact is 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_Q","associated_paper_meta":{"channel":"crossref-senior","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"green","source":"paper_resolver"}}}