{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd5","dataset_id":"nm000313","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":24,"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":"8e3a47d4e8444ce8e056452f639d5b6e0916dd94fdcbf03574efafb4d7da931e","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 S2)","readme":"Mainsah2025-S2\n==============\nBigP3BCI Study S2 — 9x8 house/tool paradigm (24 healthy subjects).\nDataset Overview\n----------------\n  Code: Mainsah2025-S2\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 24\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: 24\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":1202591786,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000313","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:52.864418+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:04:17Z"},"total_files":288,"computed_title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study S2)","nchans_counts":[{"val":32,"count":288}],"sfreq_counts":[{"val":256.0000766323896,"count":288}],"stats_computed_at":"2026-05-01T13:49:34.659618+00:00","total_duration_s":48094.86060303087,"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":["Attention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"The Parkinson's cross-modal oddball and TBI auditory oddball examples map target-focused oddball tasks to Attention; they add Clinical because their participants were recruited for a condition. The healthy visual-task examples support labeling stimulus channel separately from research purpose. No example is an exact P300-speller match.","metadata_analysis":"The readme identifies the cohort as \"24 healthy subjects\" and states \"Health status: healthy\". It describes a \"9x8 house/tool paradigm\", \"Paradigm: p300\", and \"Class labels: Target, NonTarget\". Both target and non-target HED annotations specify \"Visual-presentation\"; the tags also say \"Modality: visual\". The application is listed as \"speller\", while an automatically generated tag says \"Type: perception\".","paper_abstract_analysis":"The abstract describes a P300 speller that detects responses to a user \"attending to relevant or target stimulus events\" and evaluates spelling performance. This supports target-directed attention as the task construct, although the paper emphasizes improving BCI stimulus presentation and performance.","evidence_alignment_check":"Pathology: Metadata says \"24 healthy subjects\" and \"Health status: healthy\". Few-shot healthy task cohorts suggest Healthy; ALIGN. Modality: Metadata labels both event classes \"Visual-presentation\" and says \"Modality: visual\". Few-shot examples assign modality by presented stimulus; ALIGN on Visual. Type: Metadata says \"Paradigm: p300\" and \"Class labels: Target, NonTarget\"; its generated tag says \"Type: perception\". Oddball few-shot examples suggest Attention for target-directed paradigms. The type tag CONFLICTS with that mapping, but it is a proposed category rather than a population or stimulus fact. The target-focused task and abstract's explicit attending language favor Attention. No explicit clinical-population fact is overridden.","decision_summary":"Pathology—Healthy versus Unknown: Healthy wins on \"24 healthy subjects\" and \"Health status: healthy\"; confidence 0.8 (two explicit quotes). Modality—Visual versus Multisensory: Visual wins because both target and non-target events say \"Visual-presentation\" and the tags say \"Modality: visual\"; no second channel is documented; confidence 0.9 (three supporting metadata features and visual-task analogs). Type—Attention versus Perception: Attention wins because \"Paradigm: p300\", \"Class labels: Target, NonTarget\", and \"Applications: speller\" describe target-focused BCI use, consistent with oddball examples and the abstract. Perception remains plausible because of \"Type: perception\", so confidence is 0.8 rather than 0.9."}},"canonical_name":null,"name_confidence":0.66,"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_S2","associated_paper_meta":{"channel":"crossref-senior","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"green","source":"paper_resolver"}}}