{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3438","dataset_id":"ds006107","associated_paper_doi":null,"authors":["Naoto Kuroda","Eishi Asano","Nobukazu Nakasato"],"bids_version":"1.7.0","contact_info":["Naoto Kuroda"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006107.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":166,"ages":[12,8,10,15,5,20,17,6,10,11,17,13,11,14,5,8,19,5,13,9,12,10,11,4,10,16,15,16,10,14,7,28,17,17,30,10,4,9,21,12,28,11,10,16,6,19,12,44,10,15,4,10,12,8,14,14,6,4,8,14,37,19,11,14,17,14,12,11,27,10,19,16,37,13,14,8,15,15,5,15,4,11,18,9,17,7,14,13,19,13,17,9,41,6,12,16,8,14,13,17,5,10,16,15,7,14,5,16,13,11,10,17,14,8,9,17,12,11,12,11,23,5,13,4,16,8,5,5,7,16,15,8,14,5,13,9,13,11,13,20,35,18,35,15,26,22,16,35,32,14,12,27,20,22,37,21,34,28,19,36,14,21,39,27,21,42],"age_min":4,"age_max":44,"age_mean":15.042168674698795,"species":null,"sex_distribution":{"m":84,"f":82},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006107","osf_url":null,"github_url":null,"paper_url":null},"funding":["N/A"],"ingestion_fingerprint":"e3d0531a207f1d990848bb5462bbc1ab0565bab86e6d4613636761d18345cc82","license":"CC0","n_contributing_labs":null,"name":"iEEG_Neural_spatial_volatility","readme":null,"recording_modality":["ieeg"],"senior_author":"Nobukazu Nakasato","sessions":["01","02"],"size_bytes":12778446176,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["sws"],"timestamps":{"digested_at":"2026-04-22T12:29:08.088340+00:00","dataset_created_at":"2025-04-08T20:12:46.118Z","dataset_modified_at":"2025-04-09T06:47:24.000Z"},"total_files":167,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006107","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"af1fad372caef61a","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.5,"modality":0.7,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot examples by paradigm/modality are:\n- The pediatric epilepsy HFO dataset (Pathology=Epilepsy, Modality=Sleep, Type=Clinical/Intervention) because it explicitly involves sleep EEG (“first 3 hours of sleep EEG data… N3 sleep stages”). This example guides the convention that explicit sleep-stage recordings map to Modality=Sleep and often Type=Sleep/Clinical depending on focus.\n- The Surrey cEEGrid sleep dataset (Pathology=Healthy, Modality=Sleep, Type=Sleep) shows the convention that when the recording is sleep itself (not an external stimulus-driven task), Modality=Sleep and Type=Sleep are appropriate.\nHowever, unlike the epilepsy HFO example, the current dataset metadata does not explicitly state a recruiting diagnosis (e.g., epilepsy). Per the override rule, we cannot assign a pathology label solely from the presence of iEEG without explicit diagnostic metadata.","metadata_analysis":"Key metadata facts available are sparse:\n- Title indicates intracranial EEG: \"title\": \"iEEG_Neural_spatial_volatility\".\n- Task label suggests a sleep stage: \"tasks\": [\"sws\"].\n- Participant demographics only: \"Subjects: 166\" and \"Age range: 4-44\".\nNo metadata snippet explicitly names a clinical condition (e.g., epilepsy) or a non-clinical recruitment description (e.g., healthy volunteers), and no stimulus modality beyond the sleep-stage/task label is described.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only \"iEEG\" in the title (\"iEEG_Neural_spatial_volatility\") and demographics (\"Subjects: 166... Age range: 4-44\"), with no diagnosis stated.\n- Few-shot pattern suggests: iEEG datasets are often epilepsy/surgical-monitoring cohorts (implicitly similar to the epilepsy sleep HFO example), but that is an inference rather than an explicit fact here.\n- Alignment: CONFLICT/INSUFFICIENT—few-shot/field convention suggests Epilepsy, but metadata does not state it. Metadata-fact rule wins → label as Unknown.\n\nModality:\n- Metadata says: task is \"sws\".\n- Few-shot pattern suggests: sleep-stage paradigms map to Modality=Sleep (as in both sleep examples).\n- Alignment: ALIGN → Sleep.\n\nType:\n- Metadata says: only \"sws\" with no further task/stimulus goals.\n- Few-shot pattern suggests: when the core state is sleep (not a perception/decision task), Type=Sleep (Surrey sleep example).\n- Alignment: ALIGN → Sleep.","decision_summary":"Top-2 comparative selection:\n\nPathology candidates:\n1) Unknown (selected): Supported by lack of explicit diagnostic recruitment info; metadata only provides \"iEEG\" title and demographics with no condition named.\n2) Epilepsy: Plausible by convention because iEEG commonly comes from epilepsy monitoring; loosely analogous to the epilepsy sleep HFO few-shot example, but not explicitly stated here.\nDecision: Choose Unknown because explicit diagnosis is not present in metadata (metadata facts override inferred conventions).\n\nModality candidates:\n1) Sleep (selected): Supported by \"tasks\": [\"sws\"], consistent with sleep-stage recordings.\n2) Resting State: Possible if \"sws\" were misinterpreted as a resting acquisition label, but \"sws\" most directly denotes slow-wave sleep.\nDecision: Sleep.\n\nType candidates:\n1) Sleep (selected): \"sws\" indicates sleep physiology as the main state being recorded.\n2) Resting-state: Alternative if the dataset were simply passive recording, but the specific sleep-stage label favors Sleep.\n\nConfidence justification:\n- Pathology confidence is low because there are zero explicit diagnosis quotes (only indirect iEEG implication).\n- Modality/Type confidence are moderate-high because the task label \"sws\" directly supports Sleep for both categories, and few-shot sleep examples align with this mapping."}},"computed_title":"iEEG_Neural_spatial_volatility","nchans_counts":[{"val":128,"count":30},{"val":112,"count":19},{"val":104,"count":7},{"val":108,"count":6},{"val":118,"count":6},{"val":120,"count":5},{"val":124,"count":5},{"val":106,"count":5},{"val":132,"count":5},{"val":102,"count":5},{"val":100,"count":5},{"val":138,"count":4},{"val":130,"count":4},{"val":58,"count":4},{"val":140,"count":3},{"val":116,"count":3},{"val":110,"count":3},{"val":136,"count":2},{"val":64,"count":2},{"val":150,"count":2},{"val":114,"count":2},{"val":126,"count":2},{"val":34,"count":2},{"val":144,"count":2},{"val":72,"count":2},{"val":84,"count":2},{"val":86,"count":2},{"val":98,"count":2},{"val":54,"count":1},{"val":69,"count":1},{"val":82,"count":1},{"val":156,"count":1},{"val":73,"count":1},{"val":38,"count":1},{"val":164,"count":1},{"val":80,"count":1},{"val":44,"count":1},{"val":96,"count":1},{"val":28,"count":1},{"val":88,"count":1},{"val":90,"count":1},{"val":46,"count":1},{"val":52,"count":1},{"val":68,"count":1},{"val":70,"count":1},{"val":122,"count":1},{"val":134,"count":1},{"val":78,"count":1},{"val":74,"count":1},{"val":133,"count":1},{"val":109,"count":1},{"val":48,"count":1},{"val":56,"count":1},{"val":94,"count":1}],"sfreq_counts":[{"val":1000.0,"count":167}],"stats_computed_at":"2026-04-22T23:16:00.311330+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.31,"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":"Kuroda2025"}}