{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33e6","dataset_id":"ds005416","associated_paper_doi":"10.3390/brainsci14111126","authors":["Yan Wu","Chunguang Tao","Qi Li"],"bids_version":"1.1.1","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds005416.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":23,"ages":[24,23,24,23,24,23,27,24,22,25,23,24,22,24,24,23,25,26,24,24,26,23,23],"age_min":22,"age_max":27,"age_mean":23.91304347826087,"species":null,"sex_distribution":{"m":13,"f":10},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://www.mdpi.com/2076-3425/14/11/1126/pdf?version=1730973354"},"funding":["Jilin Scientific and Technology Development Program (grant no. 20240101358JC)"],"ingestion_fingerprint":"f90756022286e2cb00b31c070ab530f042a625c4cd3652b64162c932be68565e","license":"CC0","n_contributing_labs":null,"name":"Fatigue Characterization of EEG under Mixed Reality Stereo Vision","readme":"In this study, we selected 24 electrodes for EEG recording: Fp1, Fp2, AF3, AF4, F7, Fz, F8, FC5, FC6 (frontal), FT7, FT8 (temporal), C3, Cz, C4, CP3, CP4 (central), P3, Pz, P4, PO3, PO4 (parietal), and O1, Oz, O2 (occipital).\nEach participant was required to complete watching 2 resting scenes and 15 movement scenes.\nA rating scene appeared to rate each exercise scene watched.\nEach movement scene consisted of 20 trials of reciprocal periodic movements at a fixed depth and velocity.\nWe focused on analyzing EEG data from watching resting scenes.\nResearchers can use this EEG data to do resting-state analysis (corresponding to events '11' and '13') as well as task-state analysis (corresponding to event '12').","recording_modality":["eeg"],"senior_author":null,"sessions":[],"size_bytes":22869412103,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["WatchingTask"],"timestamps":{"digested_at":"2026-05-31T16:18:46.234343+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":23,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005416","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"dcbcc2ae4ebb1297","model":"openai/gpt-5.2","tagged_at":"2026-01-20T17:54:39.403529+00:00"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Resting-state"],"confidence":{"pathology":0.6,"modality":0.7,"type":0.7},"reasoning":{"few_shot_analysis":"Closest convention match is the few-shot example \"A Resting-state EEG Dataset for Sleep Deprivation\" (Healthy + Resting State + Resting-state): it demonstrates that when metadata explicitly frames the data as \"resting-state\" (even if recorded in a controlled lab setting), the Type label should be \"Resting-state\". For stimulus modality, the motor/imagery example (\"EEG Motor Movement/Imagery Dataset\") shows that motor-related labels are used when movement execution/imagery is the core experimental content; here, however, the described paradigm is explicitly \"watching\" scenes, aligning more with Visual stimulation conventions rather than Motor modality.","metadata_analysis":"Key quoted facts from the provided README:\n1) Task/stimulus context: \"Each participant was required to complete watching 2 resting scenes and 15 movement scenes.\"\n2) Analysis focus: \"We focused on analyzing EEG data from watching resting scenes.\"\n3) Explicit labeling of conditions: \"Researchers can use this EEG data to do resting-state analysis (corresponding to events '11' and '13') as well as task-state analysis (corresponding to event '12').\"","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: no diagnosis/clinical recruitment is mentioned (no patient group described).\n- Few-shot pattern suggests: absent clinical terms typically maps to \"Healthy\" (e.g., the resting-state sleep deprivation example uses Healthy when no disorder recruitment is indicated).\n- Alignment: ALIGN (both point away from clinical labels), but metadata lacks an explicit \"healthy\" statement.\n\nModality:\n- Metadata says: \"complete watching ... scenes\" (visual viewing of scenes).\n- Few-shot pattern suggests: watching/visual stimuli map to \"Visual\" modality (vs \"Resting State\" modality which is used for no-stimulus eyes-open/closed baselines).\n- Alignment: ALIGN toward Visual.\n\nType:\n- Metadata says: \"resting-state analysis (corresponding to events '11' and '13')\" and focus on \"watching resting scenes\".\n- Few-shot pattern suggests: when a dataset’s primary intended use is resting-state analysis, Type should be \"Resting-state\" (as in the resting-state sleep deprivation example).\n- Alignment: ALIGN, though the presence of \"movement scenes\" makes a secondary interpretation (Motor/Perception) plausible if the task-state portion were primary.","decision_summary":"Pathology (top-2):\n1) Healthy — Supported by lack of any clinical recruitment language and typical convention in few-shot examples where non-clinical cohorts default to Healthy.\n2) Unknown — Also plausible because metadata never explicitly states \"healthy\" or \"controls\".\nFinal: Healthy. Evidence alignment: mostly aligned, but inferred.\n\nModality (top-2):\n1) Visual — Strongly supported by: \"required to complete watching ... scenes\".\n2) Resting State — Possible only if the 'resting scenes' were truly no-stimulus rest, but metadata indicates viewing scenes.\nFinal: Visual. Evidence alignment: aligned.\n\nType (top-2):\n1) Resting-state — Explicitly stated: \"do resting-state analysis (events '11' and '13')\" and they \"focused on\" resting scenes.\n2) Perception — Also plausible because the experimental act is passive viewing of scenes; however, the dataset framing emphasizes resting-state analysis rather than perceptual discrimination.\nFinal: Resting-state. Evidence alignment: aligned, but limited detail about exact resting condition."}},"nemar_citation_count":0,"computed_title":"Fatigue Characterization of EEG under Mixed Reality Stereo Vision","nchans_counts":[{"val":64,"count":23}],"sfreq_counts":[{"val":1000.0,"count":23}],"stats_computed_at":"2026-05-31T19:34:32.601145+00:00","source_url":"https://openneuro.org/datasets/ds005416","total_duration_s":88847.0,"author_year":"Wu2024","canonical_name":null,"bad_channels_info":null,"acknowledgements":"Thanks to Mengru Du, Qingyu Na, Yuanyuan Wang, Dianfei Zhao and Tianqi Mu for their assistance with the data collection","ethics_approvals":["The study complied with the Declaration of Helsinki and was conducted by the approval of the Ethics Committee of the University Institutional Review Board of Changchun University of Science and Technology"],"how_to_acknowledge":"If you use this dataset in your research, please include the following acknowledgment:  \n\"We gratefully acknowledge the use of the EEG dataset provided by Wu et al. (2024) through OpenNeuro.\"\n","associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":1,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}