{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cd3","dataset_id":"nm000310","associated_paper_doi":"10.1038/s41597-025-04861-9","authors":["Eva Guttmann-Flury","Xinjun Sheng","Xiangyang Zhu"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.1038/s41597-025-04861-9","datatypes":["eeg"],"demographics":{"subjects_count":11,"ages":[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":5,"m":6},"handedness_distribution":{"r":9,"l":1}},"experimental_modalities":null,"external_links":{"paper_url":"https://www.nature.com/articles/s41597-025-04861-9.pdf"},"funding":[],"ingestion_fingerprint":"921d7c87c87d09af163d8ef9a93068234c927184228a9f777d9ca8810f51ab00","license":"CC0","n_contributing_labs":null,"name":"Guttmann-Flury et al. 2025 (SSVEP) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","readme":"GuttmannFlury2025-SSVEP\n=======================\nEye-BCI multimodal SSVEP dataset from Guttmann-Flury et al 2025.\nDataset Overview\n----------------\n  Code: GuttmannFlury2025-SSVEP\n  Paradigm: ssvep\n  DOI: 10.1038/s41597-025-04861-9\n  Subjects: 31\n  Sessions per subject: 3\n  Events: 10.0=1, 11.0=2, 12.0=3, 13.0=4\n  Trial interval: [0, 5] 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: ssvep\n  Number of classes: 4\n  Class labels: 10.0, 11.0, 12.0, 13.0\n  Trial duration: 7.0 s\n  Study design: Multi-paradigm BCI (MI/ME/SSVEP/P300). SSVEP: 4-class frequency flickering, 48 trials/session, up to 3 sessions per subject.\n  Feedback type: none\n  Stimulus type: flickering LED\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  10.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/10_0\n  11.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/11_0\n  12.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/12_0\n  13.0\n    ├─ Sensory-event\n    ├─ Experimental-stimulus\n    ├─ Visual-presentation\n    └─ Label/13_0\nParadigm-Specific Parameters\n----------------------------\n  Detected paradigm: ssvep\n  Stimulus frequencies: [8.0, 10.0, 12.0, 15.0] Hz\nData Structure\n--------------\n  Trials: 3024\n  Trials context: 63 sessions x 48 trials = 3024\nBCI Application\n---------------\n  Applications: communication\n  Environment: laboratory\nTags\n----\n  Pathology: Healthy\n  Modality: Visual\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":2217195111,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000310","raw_key":"dataset_description.json","dep_keys":["README","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["ssvep"],"timestamps":{"digested_at":"2026-04-30T14:09:51.589351+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-11T21:01:12Z"},"total_files":26,"computed_title":"Guttmann-Flury et al. 2025 (SSVEP) — Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms","nchans_counts":[{"val":65,"count":26}],"sfreq_counts":[{"val":1000.0,"count":26}],"stats_computed_at":"2026-05-01T13:49:34.659589+00:00","total_duration_s":11363.974,"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.8},"reasoning":{"few_shot_analysis":"The healthy motor-imagery BCI example shows that type follows the paradigm in the recordings, not the broader BCI application. The auditory brainstem-response example maps sensory-evoked responses to Perception. Here the recorded task is SSVEP, so the visual flicker paradigm is more relevant than the motor paradigms named in the paper.","metadata_analysis":"The readme states \"Health status: healthy\" and tags the dataset \"Pathology: Healthy\". It identifies the recorded \"Paradigm: ssvep\", describes \"SSVEP: 4-class frequency flickering\", and specifies \"Stimulus type: flickering LED\" and \"Stimulus modalities: visual\". HED annotations also mark the events \"Visual-presentation\". The participants overview lists 11 subjects, whereas the readme lists 31; this count discrepancy does not change the stated health status or task.","paper_abstract_analysis":"The abstract identifies steady-state visually evoked potentials as one of four paradigms and describes the broader aim as analysis of ocular activity and eye-induced artifacts. It supports classifying this SSVEP subset by its own paradigm, rather than as motor imagery or execution.","evidence_alignment_check":"Pathology: Metadata says \"Health status: healthy\"; the healthy BCI few-shot example suggests Healthy. ALIGN. Modality: Metadata says \"Stimulus modalities: visual\" and \"Stimulus type: flickering LED\"; the visually cued BCI example suggests Visual. ALIGN. Type: Metadata says \"Paradigm: ssvep\" and \"SSVEP: 4-class frequency flickering\"; the sensory-evoked-response example suggests Perception, while the BCI example cautions against labeling this subset Motor. ALIGN. The paper's broader artifact-analysis aim creates some ambiguity with Other but does not contradict the recorded SSVEP paradigm.","decision_summary":"Pathology—Healthy versus Unknown: \"Health status: healthy\" and \"Pathology: Healthy\" directly support Healthy; no recruited condition is named. Select Healthy (0.8; two explicit statements and a healthy-BCI analog). Modality—Visual versus Multisensory: \"Stimulus modalities: visual\", \"Stimulus type: flickering LED\", and \"Visual-presentation\" support Visual; multiple recording devices and other paradigms do not establish multiple stimulus channels in this subset. Select Visual (0.9; three explicit features and a BCI analog). Type—Perception versus Other: \"Paradigm: ssvep\" and \"SSVEP: 4-class frequency flickering\" support sensory-evoked visual processing, consistent with the sensory-response few-shot example. Other is plausible because ocular-artifact analysis is the broader study aim, but is less specific to these trials. Select Perception (0.8; two explicit paradigm features and a strong analog). No metadata fact was overridden by a few-shot pattern."}},"canonical_name":null,"name_confidence":0.72,"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":"GuttmannFlury2025_SSVEP","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":3,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}