{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd1","dataset_id":"nm000301","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":17,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"m":10,"f":7},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/nm000301","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"05ed315747d0384c54cbb6e17f517ee75fec26e1cd691411ac0330078883770b","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 D)","readme":"Mainsah2025-D\n=============\nBigP3BCI Study D — 6x6 dynamic/row-column (17 healthy subjects).\nDataset Overview\n----------------\n  Code: Mainsah2025-D\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 17\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: 17\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":773713103,"source":"nemar","storage":{"backend":"s3","base":"s3://openneuro.org/nm000301","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-22T12:52:20.904183+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":307,"computed_title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study D)","nchans_counts":[{"val":32,"count":307}],"sfreq_counts":[{"val":256.0,"count":307}],"stats_computed_at":"2026-04-22T23:16:00.314459+00:00","total_duration_s":30867.80078125,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"a7782374d8df087c","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.9,"modality":0.9,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by paradigm structure is the Cross-modal Oddball Task example (oddball/target vs non-target cues), which demonstrates that target-vs-standard paradigms are treated as stimulus-driven cognitive ERP tasks rather than pure motor tasks. Another relevant convention example is the DPX Cog Ctl Task (labeled Type=Attention), showing that tasks centered on cognitive control/target monitoring are mapped to Attention. Although no few-shot is a direct P300 speller, these examples guide mapping a target/non-target ERP paradigm toward Attention/Perception rather than Motor or Resting-state.","metadata_analysis":"Key metadata facts:\n- Population: \"BigP3BCI Study D — 6x6 dynamic/row-column (17 healthy subjects)\" and \"Health status: healthy\".\n- Paradigm/task: \"Paradigm: p300\" and \"Events: Target=2, NonTarget=1\".\n- Stimulus modality: HED annotations include \"Visual-presentation\" for both Target and NonTarget; also \"Tags -- Modality: visual\".\n- Task context: \"BCI Application -- Applications: speller\" and \"Online feedback: True\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"17 healthy subjects\" / \"Health status: healthy\".\n- Few-shot pattern suggests: Healthy when explicitly stated (consistent across multiple examples).\n- ALIGN.\n\nModality:\n- Metadata says: HED includes \"Visual-presentation\"; also \"Modality: visual\".\n- Few-shot pattern suggests: visual discrimination/visual oddball paradigms map to Visual modality.\n- ALIGN.\n\nType:\n- Metadata says: \"Paradigm: p300\", \"Target, NonTarget\", and \"Applications: speller\" (a classic selective-attention/oddball ERP setup); tags also say \"Type: perception\".\n- Few-shot pattern suggests: oddball/target monitoring tasks often reflect attentional target detection (cf. DPX labeled Attention; oddball paradigms emphasizing detecting rare targets commonly fit Attention/Perception).\n- PARTIAL ALIGN/AMBIGUITY: metadata's explicit tag 'Type: perception' points to Perception, but the P300 speller/target-vs-nontarget design is more strongly characterized as selective attention/target detection in EEG ERP conventions. No explicit clinical-population fact is involved, so this is a construct-level interpretation rather than an override of a factual diagnosis.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Healthy (winner) — explicit: \"17 healthy subjects\"; \"Health status: healthy\".\n2) Unknown (runner-up) — only if health status were absent (not the case).\nDecision: Healthy. Evidence alignment: ALIGN.\n\nModality:\n1) Visual (winner) — explicit: HED \"Visual-presentation\"; \"Modality: visual\"; 6x6 row/column speller implies visual flashes.\n2) Multisensory (runner-up) — would require auditory/tactile cues (not described).\nDecision: Visual. Evidence alignment: ALIGN.\n\nType:\n1) Attention (winner) — P300 speller with \"Target, NonTarget\" implies selective attention/target detection; online BCI feedback supports an attention-to-target paradigm.\n2) Perception (runner-up) — dataset tag explicitly says \"Type: perception\" and the task involves discriminating target vs non-target stimuli.\nDecision: Attention, because the primary construct of P300 speller paradigms is attentional target detection (oddball ERP) rather than broader sensory perception. Confidence moderated due to the competing explicit metadata tag."}},"canonical_name":null,"name_confidence":0.74,"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_D"}}