{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c71","dataset_id":"nm000136","associated_paper_doi":null,"authors":["Eva Guttmann-Flury","Xinjun Sheng","Xiangyang Zhu"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000136","datatypes":["eeg"],"demographics":{"subjects_count":31,"ages":[28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28,28],"age_min":28,"age_max":28,"age_mean":28.0,"species":null,"sex_distribution":{"f":29},"handedness_distribution":{"r":29}},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000136","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"0ec7b4157d7a41dbab1214c1c89f0bea8111924cdf5bb83cf1c240e2d453cc32","license":"CC0","n_contributing_labs":null,"name":"Guttmann-Flury et al. 2025 (P300) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000136-blue)](https://doi.org/10.82901/nemar.nm000136)\nGuttmannFlury2025-P300\n======================\nEye-BCI multimodal P300 speller dataset from Guttmann-Flury et al 2025.\nDataset Overview\n----------------\n  Code: GuttmannFlury2025-P300\n  Paradigm: p300\n  DOI: 10.1038/s41597-025-04861-9\n  Subjects: 31\n  Sessions per subject: 3\n  Events: Target=1, NonTarget=2\n  Trial interval: [0, 1] s\n  File format: BDF\nAcquisition\n-----------\n  Sampling rate: 1000.0 Hz\n  Number of channels: 66\n  Channel types: eeg=64, eog=1, stim=1\n  Channel names: FP1, FPZ, FP2, AF3, AF4, F7, F5, F3, F1, FZ, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, FT8, T7, C5, C3, C1, CZ, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, CP4, CP6, TP8, P7, P5, P3, P1, PZ, P2, P4, P6, P8, PO7, PO5, PO3, POZ, PO4, PO6, PO8, O1, OZ, O2, CB1, CB2\n  Montage: standard_1005\n  Hardware: Neuroscan Quik-Cap 65-ch, SynAmps2\n  Reference: right mastoid (M1)\n  Ground: forehead\n  Sensor type: Ag/AgCl\n  Line frequency: 50.0 Hz\n  Online filters: {'highpass_time_constant_s': 10}\nParticipants\n------------\n  Number of subjects: 31\n  Health status: healthy\n  Age: mean=28.3, min=20.0, max=57.0\n  Gender distribution: female=11, male=20\n  Species: human\nExperimental Protocol\n---------------------\n  Paradigm: p300\n  Number of classes: 2\n  Class labels: Target, NonTarget\n  Study design: Multi-paradigm BCI (MI/ME/SSVEP/P300). P300: row/column speller with 4L and 5L grid sizes.\n  Feedback type: none\n  Stimulus type: row-column flash\n  Stimulus modalities: visual\n  Primary modality: visual\n  Synchronicity: synchronous\n  Mode: offline\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\nData Structure\n--------------\n  Trials: 2520\n  Trials context: 63 sessions x 40 trials = 2520 (P300-4L default)\nBCI Application\n---------------\n  Applications: speller, communication\n  Environment: laboratory\nTags\n----\n  Pathology: Healthy\n  Modality: ERP\n  Type: Research\nDocumentation\n-------------\n  DOI: 10.1038/s41597-025-04861-9\n  License: CC0\n  Investigators: Eva Guttmann-Flury, Xinjun Sheng, Xiangyang Zhu\n  Institution: Shanghai Jiao Tong University\n  Country: CN\n  Publication year: 2025\nReferences\n----------\nGuttmann-Flury, E., Sheng, X., & Zhu, X. (2025). Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms. Scientific Data, 12, 587. https://doi.org/10.1038/s41597-025-04861-9\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","2"],"size_bytes":7887187627,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000136","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:08:38.647051+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-17T13:46:32Z"},"total_files":63,"computed_title":"Guttmann-Flury et al. 2025 (P300) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","nchans_counts":[{"val":65,"count":63}],"sfreq_counts":[{"val":1000.0,"count":63}],"stats_computed_at":"2026-05-01T13:49:34.644996+00:00","total_duration_s":40402.937,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"cc999beb02fa19ce","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":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot paradigm-wise is the Cross-modal Oddball Task example (Parkinson's; Multisensory; Clinical/Intervention): it uses an oddball structure with standards vs oddballs/targets. The current dataset similarly has a classic ERP target-detection structure (\"Events: Target=1, NonTarget=2\") typical of P300 speller paradigms. Few-shot conventions suggest: (a) explicit recruitment diagnosis determines Pathology (override rule), (b) stimulus channel determines Modality (e.g., dots task→Visual; auditory oddball→Auditory), and (c) oddball/target-detection paradigms commonly map to an attention/target-detection construct rather than motor output mechanics.","metadata_analysis":"Key explicit metadata facts:\n1) Population/health: \"Health status: healthy\" and also \"Tags\\n----\\n  Pathology: Healthy\".\n2) Paradigm/task: \"Eye-BCI multimodal P300 speller dataset\" and \"Paradigm: p300\".\n3) Stimulus modality: \"Stimulus modalities: visual\" + \"Primary modality: visual\" and \"Stimulus type: row-column flash\".\n4) Oddball/target structure: \"Events: Target=1, NonTarget=2\" and HED annotations include \"Visual-presentation\" under both Target and NonTarget.","paper_abstract_analysis":"No useful paper information (no abstract text provided in the metadata).","evidence_alignment_check":"Pathology:\n- Metadata says: \"Health status: healthy\" / \"Pathology: Healthy\".\n- Few-shot pattern suggests: use explicit recruited condition when stated (override rule).\n- ALIGN.\n\nModality:\n- Metadata says: \"Stimulus modalities: visual\", \"Primary modality: visual\", \"Stimulus type: row-column flash\".\n- Few-shot pattern suggests: classify by stimulus channel (e.g., visual discrimination→Visual; auditory oddball→Auditory).\n- ALIGN.\n\nType:\n- Metadata says: \"P300 speller\" with \"Events: Target=1, NonTarget=2\" (target vs nontarget detection).\n- Few-shot pattern suggests: oddball/target-detection ERP paradigms align with attentional selection/target detection (rather than Motor).\n- ALIGN (with minor ambiguity vs Perception because flashes are visual stimuli; but the task goal is target selection/attention in an ERP speller).","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n1) Healthy — Supported by: \"Health status: healthy\"; \"Pathology: Healthy\"; demographic summary under \"Participants\" with no disorder indicated. (ALIGN)\n2) Unknown — Would apply only if health/recruitment were not stated. Not supported.\nFinal: Healthy.\n\nModality:\n1) Visual — Supported by: \"Stimulus modalities: visual\"; \"Primary modality: visual\"; \"Stimulus type: row-column flash\"; HED includes \"Visual-presentation\". (ALIGN)\n2) Multisensory — Considered because dataset is described as \"Eye-BCI multimodal\" and references eye-tracking/video, but the speller stimulus input is explicitly visual.\nFinal: Visual.\n\nType:\n1) Attention — Supported by: P300 speller target selection/oddball structure: \"Events: Target=1, NonTarget=2\"; \"P300: row/column speller\"; synchronous row/column flashing requiring focusing on target.\n2) Perception — Plausible because it is visual stimulation (\"row-column flash\"), but the primary construct in P300 spellers is attentional target detection rather than sensory discrimination per se.\nFinal: Attention.\n\nConfidence justification:\n- Pathology high (3+ explicit indicators): \"Health status: healthy\" + \"Pathology: Healthy\" + no clinical recruitment described.\n- Modality high (3+ explicit indicators): \"Stimulus modalities: visual\" + \"Primary modality: visual\" + \"Stimulus type: row-column flash\" (+ HED \"Visual-presentation\").\n- Type moderate-high: explicit P300 speller + Target/NonTarget structure supports Attention, but Perception remains a reasonable runner-up because the stimuli are visual flashes."}},"canonical_name":null,"name_confidence":0.74,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"GuttmannFlury2025"}}