{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cc2","dataset_id":"nm000247","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":null,"datatypes":["eeg"],"demographics":{"subjects_count":10,"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://nemar.org/dataexplorer/detail/nm000247","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"8b044ee7f64cef7acb9263a52106f359dfe8de49daf64636ebdbd00b981803bb","license":"CC-BY-4.0","n_contributing_labs":null,"name":"BigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects)","readme":"# BigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects)\nBigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects).\n## Dataset Overview\n- **Code**: Mainsah2025-S1\n- **Paradigm**: p300\n- **DOI**: 10.13026/0byy-ry86\n- **Subjects**: 10\n- **Sessions per subject**: 1\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 1.0] s\n## Acquisition\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\n## Participants\n- **Number of subjects**: 10\n- **Health status**: healthy\n## Experimental Protocol\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n## HED Event Annotations\nSchema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser\n```\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\n```\n## Paradigm-Specific Parameters\n- **Detected paradigm**: p300\n## Signal Processing\n- **Feature extraction**: P300_ERP_detection\n## Cross-Validation\n- **Method**: calibration-then-test\n- **Evaluation type**: within_subject\n## BCI Application\n- **Applications**: speller\n- **Environment**: laboratory\n- **Online feedback**: True\n## Tags\n- **Modality**: visual\n- **Type**: perception\n## Documentation\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\n## References\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":501118858,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000247","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:38.344071+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-26T01:50:31Z"},"total_files":120,"computed_title":"BigP3BCI Study S1 — 9x8 face/house paradigm (10 healthy subjects)","nchans_counts":[{"val":32,"count":120}],"sfreq_counts":[{"val":256.0000766323896,"count":120}],"stats_computed_at":"2026-05-01T13:49:34.646318+00:00","total_duration_s":20039.525251262865,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"3c8f5a46991544c2","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.75},"reasoning":{"few_shot_analysis":"Most similar few-shot paradigms are the oddball/P300-style target-vs-nontarget tasks. Example: “Cross-modal Oddball Task.” (Parkinson’s) uses an oddball structure with standard vs oddball cues and is labeled with a cognitive-task type rather than resting-state. Example: “EEG: Three-Stim Auditory Oddball…” (TBI) also uses target/standard/novel tones and is treated as an active cognitive paradigm with stimulus-driven target detection. These examples guide the convention that P300/oddball target-detection datasets should be labeled by the stimulus modality (auditory/visual/multisensory) and by an attention/target-detection construct (rather than Motor or Resting-state).","metadata_analysis":"Key population facts: title states “(10 healthy subjects)” and README states “Health status: healthy”.\nKey task/paradigm facts: README states “Paradigm: p300” and “Events: Target=2, NonTarget=1”, and also “Applications: speller” with “Online feedback: True”.\nKey stimulus modality facts: title/README indicate a “9x8 face/house paradigm” and the HED annotation for both Target and NonTarget includes “Visual-presentation”; README also explicitly lists “Modality: visual”.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata SAYS: “(10 healthy subjects)” and “Health status: healthy”.\n- Few-shot pattern SUGGESTS: when explicitly healthy, label Healthy.\n- ALIGNMENT: Align.\n\nModality:\n- Metadata SAYS: “Modality: visual”, “9x8 face/house paradigm”, and HED includes “Visual-presentation”.\n- Few-shot pattern SUGGESTS: oddball/P300 tasks are labeled by stimulus channel; visual stimuli -> Visual.\n- ALIGNMENT: Align.\n\nType:\n- Metadata SAYS: “Paradigm: p300”, “Events: Target… NonTarget…”, and “Applications: speller” (classic P300 BCI target-attention paradigm).\n- Few-shot pattern SUGGESTS: oddball/P300 target-detection paradigms are categorized as an active cognitive construct (often attention/target detection) rather than resting-state.\n- ALIGNMENT: Mostly align; metadata includes an internal tag “Type: perception”, but the paradigm description (P300 target vs nontarget, speller) more directly supports an attention/target-detection construct. No conflict with any explicit clinical-population facts.","decision_summary":"Top-2 candidates (with head-to-head comparison):\n\nPathology: Healthy vs Unknown\n- Healthy evidence: “(10 healthy subjects)”; “Health status: healthy”.\n- Unknown evidence: none.\n=> Select Healthy. Confidence based on 2 explicit quotes.\n\nModality: Visual vs Other\n- Visual evidence: “Modality: visual”; “9x8 face/house paradigm”; HED includes “Visual-presentation”.\n- Other evidence: none.\n=> Select Visual. Confidence based on 3 explicit modality indicators.\n\nType: Attention vs Perception\n- Attention evidence: “Paradigm: p300”; “Events: Target… NonTarget…”; “Applications: speller” (P300 speller relies on attending to rare targets).\n- Perception evidence: README tag “Type: perception” (but appears to be a generic tag rather than the primary construct).\n=> Select Attention because the P300 target-detection/speller paradigm is fundamentally an attentional oddball construct. Confidence reflects that “Type: perception” is a plausible alternative label but is weaker than the P300/target-nontarget evidence."}},"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":"canonical","author_year":"Mainsah2025_BigP3BCI_S1"}}