{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3307","dataset_id":"ds004012","associated_paper_doi":null,"authors":["Nur Syairah Ab Rani","Nurfaizatul Aisyah Ab Aziz","Mohammed Farouq Reza","Muzaimi Mustapha"],"bids_version":"1.6.0","contact_info":["MUHAMMAD HAKIMI BIN MOHD. RASHID"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004012.v1.0.0","datatypes":["meg"],"demographics":{"subjects_count":30,"ages":[28,29,34,23,26,26,24,22,31,25,24,22,24,24,28,31,22,27,28,58,24,22,27,25,24,23,22,24,24,22],"age_min":22,"age_max":58,"age_mean":26.433333333333334,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004012","osf_url":null,"github_url":null,"paper_url":null},"funding":["The study has been funded by the USM Research Grant for Fundamental Neuroscience–Neurobehaviour (BrainReward and Anti-Reward) (1002/CNEURO/910114) and USM Research University Grant (1001.PPSP.812189)"],"ingestion_fingerprint":"d78ed67d9b4f2c991aafefacb3a162a9857dbfe4156f8c72340c7440ce2e6284","license":"CC0","n_contributing_labs":null,"name":"BRAR_NQ","readme":"References\n----------\nAppelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, 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\nNiso, G., Gorgolewski, K. J., Bock, E., Brooks, T. L., Flandin, G., Gramfort, A., Henson, R. N., Jas, M., Litvak, V., Moreau, J., Oostenveld, R., Schoffelen, J., Tadel, F., Wexler, J., Baillet, S. (2018). MEG-BIDS, the brain imaging data structure extended to magnetoencephalography. Scientific Data, 5, 180110. https://doi.org/10.1038/sdata.2018.110","recording_modality":["meg"],"senior_author":"Muzaimi Mustapha","sessions":[],"size_bytes":84108428161,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["rest01","rest02","stim01","stim02","stim03","stim04","stim05","stim06","stim07","stim08"],"timestamps":{"digested_at":"2026-04-22T12:25:56.721109+00:00","dataset_created_at":"2022-02-03T09:47:39.342Z","dataset_modified_at":"2022-02-01T02:24:04.000Z"},"total_files":294,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004012","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-auditorystimuli_events.json"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"b1d05c8f4ee8d542","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Unknown"],"confidence":{"pathology":0.7,"modality":0.4,"type":0.4},"reasoning":{"few_shot_analysis":"Closest few-shot conventions are the resting-state healthy datasets (e.g., “A Resting-state EEG Dataset for Sleep Deprivation” labeled Healthy / Resting State / Resting-state) where the task is explicitly rest. BRAR_NQ includes tasks named “rest01” and “rest02”, which stylistically matches how resting recordings are labeled as Resting State modality and Resting-state type when no active paradigm is described. However, this dataset also includes multiple “stimXX” tasks without any description of stimulus content, unlike the few-shot auditory stimulus dataset (“Subcortical responses to music and speech…”) where the auditory modality and perception-type are clearly stated. Therefore few-shot examples guide that: (a) clear ‘rest’ → Resting State, but (b) ‘stim’ without details is not enough to infer auditory/visual/etc.","metadata_analysis":"Available metadata is minimal and does not describe participant diagnosis or the stimulation paradigm. Key snippets: (1) participants: \"Subjects: 30; Age range: 22-58\" (no clinical recruitment mentioned). (2) tasks list includes both rest and stimulation runs: \"tasks\": [\"rest01\", \"rest02\", \"stim01\", \"stim02\", \"stim03\", \"stim04\", \"stim05\", \"stim06\", \"stim07\", \"stim08\"]. (3) README contains only BIDS references and no task details: \"References ... MNE-BIDS... MEG-BIDS...\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says nothing about a disorder (\"Subjects: 30; Age range: 22-58\"); few-shot pattern suggests labeling as Healthy when no clinical recruitment is stated. ALIGN.\nModality: Metadata says only \"stim01\"-\"stim08\" with no stimulus description; few-shot patterns require explicit mention (e.g., clicks/music/speech for Auditory; dots/targets for Visual). CONFLICT/INSUFFICIENT: no fact to align; cannot infer modality from task names alone.\nType: Metadata indicates both rest and stim runs (\"rest01\", \"rest02\", \"stim01\"...); few-shot conventions would label Resting-state only if the dataset is purely rest and described as such. Here stimulation exists but is undefined, so the dataset purpose (perception/attention/etc.) cannot be determined. CONFLICT/INSUFFICIENT: no fact to align; cannot select a specific cognitive construct.","decision_summary":"Pathology top-2: (1) Healthy — supported by absence of clinical descriptors and generic demographic line \"Subjects: 30; Age range: 22-58\". (2) Unknown — possible because recruitment criteria are not stated. Winner: Healthy.\nModality top-2: (1) Resting State — supported by tasks \"rest01\", \"rest02\". (2) Unknown — because \"stim01\"-\"stim08\" are undefined and could dominate. Winner: Unknown (cannot determine dominant stimulus channel across the whole dataset).\nType top-2: (1) Resting-state — plausible due to explicit rest tasks (\"rest01\", \"rest02\"). (2) Unknown — stimulation tasks exist but no paradigm/cognitive aim is described. Winner: Unknown.\nConfidence justification: Pathology has one explicit demographic snippet and no competing pathology evidence; Modality and Type lack any explicit stimulus/task-description quotes beyond run names, so confidence must remain low."}},"nemar_citation_count":1,"computed_title":"BRAR_NQ","nchans_counts":[{"val":383,"count":294}],"sfreq_counts":[{"val":1000.0,"count":294}],"stats_computed_at":"2026-04-22T23:16:00.306750+00:00","total_duration_s":54058.706,"canonical_name":null,"name_confidence":0.55,"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":"Rani2022"}}