{"success":true,"database":"eegdash","data":{"_id":"696fdefaac44fa1028dc631b","dataset_id":"ds007119","associated_paper_doi":null,"authors":["Keisuke Hatano","Naoto Kuroda","Hiroshi Uda","Kazuki Sakakura","Michael J. Cools","Aimee F. Luat","Shin-Ichiro Osawa","Hitoshi Nemoto","Kazushi Ukishiro","Hidenori Endo","Nobukazu Nakasato","Yutaro Takayama","Keiya Iijima","Masaki Iwasaki","Eishi Asano"],"bids_version":"1.7.0","contact_info":["Keisuke Hatano"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007119.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":103,"ages":[12,8,10,15,5,17,6,10,11,17,13,11,14,8,19,5,13,9,12,11,4,10,16,15,16,10,14,7,17,17,30,10,4,9,22,12,11,10,16,6,19,12,10,15,4,10,12,8,14,14,6,4,8,14,37,19,21,14,5,12,11,10,19,16,13,14,8,15,5,15,4,11,18,10,17,7,14,13,19,13,17,9,41,6,12,16,8,14,14,17,5,10,16,15,7,14,5,16,13,11,10,17,14,8,9,17,12,11,13,11,5,13,4,16,11,5,5,7,16,15,8,14,5,13,11,8,3,2,2,2,2,2,7,1,1,1,3,1,2,2,1,1,3,1,1,2,9,13,11,13,20,15,6,13,11,17,13,7,8,6,3,3,3,3,3,45,3,3,3,1,2,16,12,8,35,18,35,15,26,22,16,35,32,14,12,27,20,22,37,21,34,28,19,36,14,21,39,27,21,42,44,15,14,32,15,23,12,8,18,38,31,26,7,33,25,36,25,6,45,14,36,11,19,9,20,5,21,11,12,16],"age_min":1,"age_max":45,"age_mean":13.656521739130435,"species":null,"sex_distribution":{"f":119,"m":114},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007119","osf_url":null,"github_url":null,"paper_url":null},"funding":["the National Institutes of Health (NIH; NS064033 to E.A.)","the Uehara Memorial Foundation Postdoctoral Fellowship (202441017 to K.H.; 20210301 to H.U.)","the Japan Society for the Promotion of Science (JP22J23281 and JP22KJ0323 to N.K.; 202560628 to H.U.; JP19K09494 and 22K09296 to M.I.)"],"ingestion_fingerprint":"751b848a36ac51da3758647a265fcba879d61ec33ece8784b661cf4cc116f15d","license":"CC0","n_contributing_labs":null,"name":"iEEG_comprehensive_HFA_model_part3","readme":"This dataset contains intracranial EEG data recorded during non-REM sleep and used in Hatano et al. (in press).\nAuthors:\nKeisuke Hatano, Naoto Kuroda, Hiroshi Uda, Kazuki Sakakura, Michael J. Cools, Aimee F. Luat, Shin-Ichiro Osawa, Hitoshi Nemoto, Kazushi Ukishiro, Hidenori Endo, Nobukazu Nakasato, Yutaro Takayama, Keiya Iijima, Masaki Iwasaki, Eishi Asano\nFunding:\nNational Institutes of Health (NIH; NS064033 to E.A.);\nUehara Memorial Foundation Postdoctoral Fellowship (202441017 to K.H.; 20210301 to H.U.);\nJapan Society for the Promotion of Science (JP22J23281, JP22KJ0323, and 202560576 to N.K.; 202560628 to H.U.; JP19K09494 and 22K09296 to M.I.)","recording_modality":["ieeg"],"senior_author":"Eishi Asano","sessions":["01"],"size_bytes":34981740938,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007119","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["sleep"],"timestamps":{"digested_at":"2026-04-22T12:30:02.254947+00:00","dataset_created_at":"2025-12-19T23:33:30.802Z","dataset_modified_at":"2025-12-22T04:54:04.000Z"},"total_files":106,"computed_title":"iEEG_comprehensive_HFA_model_part3","nchans_counts":[{"val":128,"count":12},{"val":124,"count":5},{"val":134,"count":4},{"val":86,"count":4},{"val":102,"count":4},{"val":120,"count":4},{"val":78,"count":4},{"val":58,"count":4},{"val":94,"count":4},{"val":110,"count":3},{"val":118,"count":3},{"val":100,"count":3},{"val":130,"count":2},{"val":64,"count":2},{"val":84,"count":2},{"val":132,"count":2},{"val":34,"count":2},{"val":108,"count":2},{"val":140,"count":2},{"val":74,"count":2},{"val":112,"count":2},{"val":96,"count":2},{"val":148,"count":2},{"val":72,"count":2},{"val":136,"count":2},{"val":126,"count":1},{"val":180,"count":1},{"val":6,"count":1},{"val":122,"count":1},{"val":44,"count":1},{"val":135,"count":1},{"val":146,"count":1},{"val":142,"count":1},{"val":116,"count":1},{"val":152,"count":1},{"val":54,"count":1},{"val":46,"count":1},{"val":144,"count":1},{"val":76,"count":1},{"val":90,"count":1},{"val":104,"count":1},{"val":138,"count":1},{"val":88,"count":1},{"val":82,"count":1},{"val":38,"count":1},{"val":70,"count":1},{"val":73,"count":1},{"val":114,"count":1},{"val":52,"count":1},{"val":48,"count":1},{"val":28,"count":1}],"sfreq_counts":[{"val":1000.0,"count":106}],"stats_computed_at":"2026-04-22T23:16:00.312343+00:00","total_duration_s":null,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"671e47c9bfb5575f","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.45,"modality":0.85,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot examples are: (1) \"Surrey cEEGrid sleep data set\" (Healthy / Sleep / Sleep): establishes that when recordings are collected during sleep with no external stimuli, Modality and Type should both be labeled \"Sleep\". (2) \"Dataset of EEG recordings of pediatric patients with epilepsy ... first 3 hours of sleep EEG\" (Epilepsy / Resting State / Clinical/Intervention): shows a convention that sleep recordings used as clinical biomarker data can still be categorized under sleep-related labels, and that explicit pathology terms (e.g., \"epilepsy\") should drive Pathology. In the present dataset, we match the sleep recording aspect strongly, but we lack an explicit pathology statement like in the epilepsy example, so we do not copy the epilepsy label.","metadata_analysis":"Key metadata facts: (1) Recording state: \"intracranial EEG data recorded during non-REM sleep\" (README). (2) Task listing: tasks includes \"sleep\" (tasks field). (3) Dataset identity suggests invasive clinical recording but without diagnosis: title \"iEEG_comprehensive_HFA_model_part3\" and README phrase \"intracranial EEG\" indicate iEEG (typically clinical implant cases), but there is no quoted diagnosis (e.g., epilepsy) or recruitment description of patients vs healthy controls.","paper_abstract_analysis":"No useful paper information (no abstract provided in the metadata snippet).","evidence_alignment_check":"Pathology: Metadata says \"intracranial EEG\" and \"non-REM sleep\" but does NOT state any diagnosis (no mention of epilepsy, tumor, etc.). Few-shot patterns suggest iEEG datasets are often epilepsy/surgical cohorts, but that would be an inference; with no explicit diagnosis fact, we label Pathology as Unknown. (ALIGN: no conflict; metadata is simply insufficient.)\nModality: Metadata says \"recorded during non-REM sleep\" and task is \"sleep\"; few-shot sleep examples map this to Modality=\"Sleep\". (ALIGN.)\nType: Metadata indicates sleep recording and analysis context (non-REM sleep) rather than a cognitive task; few-shot sleep examples map this to Type=\"Sleep\". (ALIGN.)","decision_summary":"Top-2 candidates per category:\n1) Pathology: (A) Unknown — supported by lack of explicit recruitment/diagnosis statement; quotes: \"intracranial EEG... during non-REM sleep\" does not specify condition. (B) Epilepsy — plausible inference because iEEG commonly comes from presurgical epilepsy monitoring, but not explicitly stated anywhere. Winner: Unknown (evidence insufficient for a specific disorder). Confidence 0.45 because the runner-up (Epilepsy) is plausible but not directly supported by quoted metadata.\n2) Modality: (A) Sleep — supported by quotes: \"recorded during non-REM sleep\" and tasks=[\"sleep\"]. (B) Resting State — could apply if it were quiet wakefulness, but metadata explicitly says sleep. Winner: Sleep. Confidence 0.85 (2 explicit metadata supports + strong few-shot alignment with sleep datasets).\n3) Type: (A) Sleep — primary purpose/construct is sleep physiology (non-REM iEEG). (B) Clinical/Intervention — possible because iEEG is typically clinical, but metadata does not frame an intervention/clinical trial and emphasizes sleep state. Winner: Sleep. Confidence 0.8 (clear metadata about sleep state + few-shot sleep convention)."}},"canonical_name":null,"name_confidence":0.43,"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":"Hatano2025_part3"}}