{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4ca8","dataset_id":"nm000211","associated_paper_doi":null,"authors":["Yufeng Zhang","Hongxin Zhang","Yixuan Li","Yijun Wang","Xiaorong Gao","Chen Yang"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":null,"datatypes":["eeg"],"demographics":{"subjects_count":15,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":{"r":15}},"experimental_modalities":null,"external_links":{"source_url":"https://nemar.org/dataexplorer/detail/nm000211","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"c1152f008d00c1138013c6d5783f5275372e4b25243b79b52c0f06167e118bae","license":"CC-BY-NC-ND-4.0","n_contributing_labs":null,"name":"RSVP ERP dataset for authentication from Zhang et al 2025","readme":"# RSVP ERP dataset for authentication from Zhang et al 2025\nRSVP ERP dataset for authentication from Zhang et al 2025.\n## Dataset Overview\n- **Code**: Zhang2025\n- **Paradigm**: p300\n- **DOI**: 10.1038/s41597-025-05378-x\n- **Subjects**: 15\n- **Sessions per subject**: 4\n- **Events**: Target=2, NonTarget=1\n- **Trial interval**: [0, 0.6] s\n- **Runs per session**: 4\n- **File format**: MATLAB (HDF5)\n## Acquisition\n- **Sampling rate**: 1000.0 Hz\n- **Number of channels**: 57\n- **Channel types**: eeg=57\n- **Channel names**: Fpz, Fp1, Fp2, AF3, AF4, AF7, AF8, Fz, F1, F2, F3, F4, F5, F6, F7, F8, FCz, FC1, FC2, FC3, FC4, FC5, FC6, FT7, FT8, Cz, C1, C2, C3, C4, C5, C6, T7, T8, CP1, CP2, CP3, CP4, CP5, CP6, TP7, TP8, Pz, P3, P4, P5, P6, P7, P8, POz, PO3, PO4, PO7, PO8, Oz, O1, O2\n- **Montage**: standard_1020\n- **Hardware**: Neuracle Neusen\n- **Reference**: CPz\n- **Ground**: AFz\n- **Line frequency**: 50.0 Hz\n## Participants\n- **Number of subjects**: 15\n- **Health status**: healthy\n- **Age**: min=22, max=26\n- **Gender distribution**: female=6, male=9\n- **Handedness**: all right-handed\n- **Species**: human\n## Experimental Protocol\n- **Paradigm**: p300\n- **Number of classes**: 2\n- **Class labels**: Target, NonTarget\n- **Trial duration**: 1.0 s\n- **Study design**: RSVP face authentication; self-face vs AI-generated faces; 4 sessions over 200 days (longitudinal)\n- **Feedback type**: none\n- **Stimulus type**: RSVP face images\n- **Stimulus modalities**: visual\n- **Primary modality**: visual\n- **Mode**: offline\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- **Stimulus onset asynchrony**: 100.0 ms\n## Data Structure\n- **Trials**: ~160 target + ~6240 nontarget per session\n- **Trials context**: per session (4 blocks x 8 sequences x 200 images)\n## Signal Processing\n- **Classifiers**: HDCA\n- **Feature extraction**: HDCA\n- **Frequency bands**: ERP_dominant=[0.0, 10.0] Hz\n## Cross-Validation\n- **Evaluation type**: within_subject\n## BCI Application\n- **Applications**: identity_authentication, target_detection\n- **Environment**: laboratory\n## Tags\n- **Pathology**: Healthy\n- **Modality**: ERP\n- **Type**: RSVP\n## Documentation\n- **DOI**: 10.1038/s41597-025-05378-x\n- **License**: CC-BY-NC-ND-4.0\n- **Investigators**: Yufeng Zhang, Hongxin Zhang, Yixuan Li, Yijun Wang, Xiaorong Gao, Chen Yang\n- **Institution**: Beijing University of Posts and Telecommunications\n- **Country**: CN\n- **Data URL**: https://figshare.com/articles/dataset/27201003\n- **Publication year**: 2025\n## References\nZhang, Y., Zhang, H., Li, Y., Wang, Y., Gao, X., & Yang, C. (2025). A longitudinal EEG dataset of event-related potential for EEG-based identity authentication. Scientific Data, 12, 1069. https://doi.org/10.1038/s41597-025-05378-x\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","3"],"size_bytes":9300613160,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000211","raw_key":"dataset_description.json","dep_keys":["README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["p300"],"timestamps":{"digested_at":"2026-04-30T14:09:05.856006+00:00","dataset_created_at":null,"dataset_modified_at":"2026-03-24T04:10:55Z"},"total_files":240,"computed_title":"RSVP ERP dataset for authentication from Zhang et al 2025","nchans_counts":[{"val":57,"count":240}],"sfreq_counts":[{"val":1000.0,"count":240}],"stats_computed_at":"2026-05-01T13:49:34.645729+00:00","total_duration_s":54081.09,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"53e1502cde843a2d","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":0.85},"reasoning":{"few_shot_analysis":"Closest few-shot paradigms are the oddball/P300-style target detection datasets, especially: (1) “Cross-modal Oddball Task” (Parkinson’s) which uses standard vs oddball cues and is explicitly an oddball paradigm; and (2) “EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI” which is also an oddball with target/standard/novel tones. These examples guide the convention that target vs non-target (oddball/P300) paradigms map primarily to an Attention-type construct (target detection/oddball attention allocation), while Pathology is determined by the recruited clinical population (PD/TBI there, but Healthy here). Modality in those examples is determined by stimulus channel (auditory vs multisensory), guiding us to label this dataset Visual because the stimuli are RSVP face images.","metadata_analysis":"Pathology/population facts: the README states “Health status: healthy” and also includes a tag line “Pathology: Healthy”; additionally participants are described as “Number of subjects: 15” with typical young-adult demographics (e.g., “Age: min=22, max=26”), consistent with a healthy volunteer cohort.\n\nTask/stimulus facts: the dataset is explicitly a P300 RSVP task: “Paradigm: p300” and “Events: Target=2, NonTarget=1”. Stimulus channel is explicitly visual: “Stimulus type: RSVP face images”, “Stimulus modalities: visual”, and “Primary modality: visual”. Purpose/application is authentication via target detection: “Study design: RSVP face authentication; self-face vs AI-generated faces” and “Applications: identity_authentication, target_detection”.","paper_abstract_analysis":"No useful paper information (abstract text not provided in the metadata payload).","evidence_alignment_check":"Pathology: Metadata says participants are healthy (e.g., “Health status: healthy”; tag “Pathology: Healthy”). Few-shot pattern suggests using the recruited diagnosis/condition when explicitly stated. ALIGN.\n\nModality: Metadata explicitly says visual stimuli (e.g., “Stimulus type: RSVP face images”; “Stimulus modalities: visual”; “Primary modality: visual”). Few-shot convention labels modality by stimulus channel (auditory for tones, tactile for braille, etc.). ALIGN.\n\nType: Metadata indicates a P300/oddball-like RSVP target detection structure (“Paradigm: p300”; “Events: Target=2, NonTarget=1”; “Applications: ... target_detection”). Few-shot conventions for oddball/target detection paradigms generally map to Attention (allocation to targets) rather than Motor/Resting-state/Sleep. ALIGN (though the dataset’s end goal is biometric authentication, the cognitive construct elicited is attentional target detection in an RSVP P300 paradigm).","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Healthy (WIN) — explicit: “Health status: healthy”; tag “Pathology: Healthy”; demographics given without any disorder recruitment.\n2) Unknown — only if health status were not stated.\nAlignment: aligns with few-shot convention to use explicit recruitment condition.\nConfidence basis: 2+ explicit healthy statements plus consistent participant description.\n\nModality:\n1) Visual (WIN) — explicit: “Stimulus modalities: visual”; “Primary modality: visual”; “Stimulus type: RSVP face images”.\n2) Other — only if stimulus channel were unclear.\nAlignment: aligns with few-shot modality-by-stimulus convention.\nConfidence basis: 3 explicit visual-stimulus quotes.\n\nType:\n1) Attention (WIN) — strong P300/oddball target detection structure: “Paradigm: p300”; “Events: Target=2, NonTarget=1”; “Applications: ... target_detection”; RSVP implies rapid attentional selection.\n2) Other — plausible because the stated aim is “identity_authentication” (an application focus rather than a classic cognitive-science construct).\nAlignment: aligns with few-shot oddball/target-detection conventions; no conflict with metadata.\nConfidence basis: 3+ explicit task structure/application quotes; moderate ambiguity due to biometric framing."}},"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":"Zhang2025_RSVP"}}