{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd5","dataset_id":"nm000313","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":24,"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/nm000313","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"256ed52de9b96ed4e9d2d29a3274ccf86508986953f2d35bda5f942fc0fd6c4a","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":1202395687,"source":"nemar","storage":{"backend":"s3","base":"s3://openneuro.org/nm000313","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-22T12:52:23.322684+00:00","dataset_created_at":null,"dataset_modified_at":null},"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":"2026-04-22T23:16:00.314515+00:00","total_duration_s":48094.86060303087,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"6237b09345d1bdd6","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot conventions are the oddball-style paradigms: (1) the Parkinson's “Cross-modal Oddball Task” example shows that target vs non-target cueing is treated as an oddball/ERP paradigm and modality is driven by the stimulus channels (visual+auditory -> Multisensory). (2) The TBI “Three-Stim Auditory Oddball” example similarly maps target/standard/novel tone detection to Auditory modality. These examples guide (a) labeling modality from stimulus presentation, and (b) treating target vs non-target detection as a Perception/oddball-type construct unless the dataset is primarily a clinical cohort (then Type -> Clinical/Intervention).","metadata_analysis":"Population facts: the README states “BigP3BCI Study S2 — 9x8 house/tool paradigm (24 healthy subjects)” and under Participants: “Health status: healthy”. Task/paradigm facts: “Paradigm: p300” with “Events: Target=2, NonTarget=1” and “BCI Application — Applications: speller”. Stimulus modality facts: HED annotations for both Target and NonTarget include “Visual-presentation”, and the Tags section explicitly says “Modality: visual”.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology — Metadata says: “24 healthy subjects” and “Health status: healthy”. Few-shot pattern suggests: when recruitment is healthy controls, label Pathology as Healthy (seen across multiple healthy examples). ALIGN.\nModality — Metadata says: HED includes “Visual-presentation” and Tags include “Modality: visual”. Few-shot pattern suggests: modality comes from stimulus channel (e.g., oddball examples label Auditory or Multisensory based on stimuli). ALIGN (Visual).\nType — Metadata says: Tags include “Type: perception” and the paradigm is “p300” with “Target” vs “NonTarget” events (oddball-like detection). Few-shot pattern suggests: oddball/target-detection paradigms are typically categorized by the cognitive construct; per instructions, “Sensory discrimination/detection ... → Perception.” ALIGN overall, though P300 can also be framed as Attention; metadata’s explicit “Type: perception” supports Perception.","decision_summary":"Pathology top-2: (1) Healthy — supported by “24 healthy subjects” and “Health status: healthy”. (2) Unknown — only if population were unspecified; not the case. Final: Healthy. Confidence basis: 2 explicit quotes.\nModality top-2: (1) Visual — supported by “Visual-presentation” (HED) and “Modality: visual” (Tags). (2) Unknown/Other — only if stimulus channel were unclear; not the case. Final: Visual. Confidence basis: 2+ explicit quotes.\nType top-2: (1) Perception — supported by explicit “Type: perception” (Tags) plus target vs non-target P300 detection (“Events: Target=2, NonTarget=1”, “Paradigm: p300”). (2) Attention — plausible because P300 speller relies on selective attention to the target. Head-to-head: metadata explicitly states Perception and the task is a detection/discrimination oddball-like paradigm, matching the Perception rule. Final: Perception. Confidence basis: 3 explicit supporting snippets, but some conceptual overlap with Attention keeps confidence below maximal."}},"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"}}