{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd2","dataset_id":"nm000303","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":18,"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":"524a703ff4dab7d3f0ffbad3dc0703b8ec4f0ec4f51fa802d620632ded08586e","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 O)","readme":"Mainsah2025-O\n=============\nBigP3BCI Study O — 9x8 supervised/checkerboard (18 ALS subjects).\nDataset Overview\n----------------\n  Code: Mainsah2025-O\n  Paradigm: p300\n  DOI: 10.13026/0byy-ry86\n  Subjects: 18\n  Sessions per subject: 2\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: 18\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","1"],"size_bytes":1040423256,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000303","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:51.522367+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:04:46Z"},"total_files":347,"computed_title":"Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study O)","nchans_counts":[{"val":32,"count":347}],"sfreq_counts":[{"val":256.0000930697907,"count":162},{"val":256.0,"count":101},{"val":256.00008203487505,"count":40},{"val":256.00012071918457,"count":12},{"val":256.0001098418278,"count":12},{"val":256.00010076264726,"count":7},{"val":256.0001184842897,"count":4},{"val":256.00010663894057,"count":3},{"val":256.00009694678226,"count":3},{"val":256.00008886963377,"count":2},{"val":256.00010131094785,"count":1}],"stats_computed_at":"2026-05-01T13:49:34.659572+00:00","total_duration_s":41392.632266581226,"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"],"confidence":{"pathology":0.7,"modality":0.9,"type":0.7},"reasoning":{"few_shot_analysis":"The Parkinson's cross-modal oddball example is the closest paradigm match: target and non-target stimuli in an oddball task support Attention, while stimulus descriptions determine Modality. Its Clinical label reflects a disease-focused analysis, which is not established here. The schizophrenia visual-discrimination example shows that a response to a visual stimulus does not by itself make a task Decision-making.","metadata_analysis":"The study-specific description says \"BigP3BCI Study O — 9x8 supervised/checkerboard (18 ALS subjects),\" but a later field says \"Health status: healthy\"; these population statements conflict. The event annotations specify \"Visual-presentation\" for both Target and NonTarget, and the application is \"speller.\" The readme also lists \"Paradigm: p300\" and \"Feature extraction: P300_ERP_detection.\" Its tag \"Type: perception\" is noted, but does not describe the primary construct as specifically as the target-attendance paradigm does.","paper_abstract_analysis":"The abstract describes a \"P300 speller\" that detects responses to \"a user attending to relevant or target stimulus events.\" This supports attention to presented stimuli. It discusses optimizing spelling performance, but does not resolve whether Study O's participants had ALS or were healthy.","evidence_alignment_check":"Pathology: Metadata SAYS \"18 ALS subjects\" and, inconsistently, \"Health status: healthy.\" Few-shot examples SUGGEST labeling a patient-and-control dataset by its recruited condition rather than Healthy. They ALIGN with ALS if the study-specific statement is correct; the two metadata statements CONFLICT. The study-specific ALS count takes precedence over the generic health-status field. Modality: Metadata SAYS \"Visual-presentation\" for Target and NonTarget and \"Modality: visual.\" The oddball few-shot SUGGESTS labeling the presented cue's sensory channel; these ALIGN on Visual. Type: Metadata SAYS \"Paradigm: p300\" with Target and NonTarget events, but also tags \"Type: perception.\" The oddball few-shot SUGGESTS Attention for target-based oddball processing. The tag and paradigm-based interpretation CONFLICT; the task description and abstract's explicit target attendance favor Attention. No explicit participant or stimulus fact is overridden by a few-shot pattern.","decision_summary":"Pathology—ALS versus Healthy: \"18 ALS subjects\" is the study-specific recruitment evidence; \"Health status: healthy\" is contradictory. Select ALS with 0.7 confidence because the conflict limits certainty. Modality—Visual versus Unknown: \"Visual-presentation\" on both event classes and \"Modality: visual\" decisively favor Visual; confidence 0.9 with a clear oddball analog. Type—Attention versus Perception: \"Paradigm: p300,\" Target/NonTarget events, and the abstract's target-attendance description favor Attention and match the oddball convention, while \"Type: perception\" supports the runner-up. Select Attention with 0.7 confidence given that competing tag."}},"canonical_name":null,"name_confidence":0.7,"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_O","associated_paper_meta":{"channel":"crossref-senior","confidence":"high","author_overlap":2,"is_oa":true,"oa_status":"green","source":"paper_resolver"}}}