{"success":true,"database":"eegdash","data":{"_id":"696fdefaac44fa1028dc631a","dataset_id":"ds007118","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.ds007118.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":65,"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/ds007118","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":"c8ee94da8fdd8bde9f27bacf395ea43a931ce5db9c38db854950dd1282753f75","license":"CC0","n_contributing_labs":null,"name":"iEEG_comprehensive_HFA_model_part1","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":36317646610,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007118","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":"2026-04-22T12:30:01.618990+00:00","dataset_created_at":"2025-12-19T22:20:32.537Z","dataset_modified_at":"2025-12-22T04:47:59.000Z"},"total_files":82,"computed_title":"iEEG_comprehensive_HFA_model_part1","nchans_counts":[{"val":128,"count":21},{"val":112,"count":17},{"val":124,"count":6},{"val":102,"count":5},{"val":120,"count":4},{"val":108,"count":4},{"val":68,"count":3},{"val":118,"count":3},{"val":138,"count":3},{"val":116,"count":3},{"val":106,"count":2},{"val":64,"count":2},{"val":144,"count":2},{"val":114,"count":1},{"val":132,"count":1},{"val":58,"count":1},{"val":122,"count":1},{"val":94,"count":1},{"val":36,"count":1},{"val":74,"count":1}],"sfreq_counts":[{"val":1000.0,"count":82}],"stats_computed_at":"2026-04-22T23:16:00.312328+00:00","total_duration_s":null,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"492802d5aa194ce9","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Sleep"],"type":["Sleep"],"confidence":{"pathology":0.4,"modality":0.85,"type":0.85},"reasoning":{"few_shot_analysis":"Closest few-shot match by paradigm is the “Surrey cEEGrid sleep data set” example (Healthy / Sleep / Sleep), which demonstrates the convention that recordings explicitly collected during sleep are labeled with Modality=Sleep and Type=Sleep when the primary purpose is sleep recording/staging rather than an active cognitive task. A secondary match is the pediatric epilepsy sleep/HFO example, which shows that sleep recordings in a clinical context can still be treated as sleep-focused; however, that example has an explicit epilepsy population statement, which this dataset lacks.","metadata_analysis":"Key metadata facts:\n1) Sleep state is explicit: “This dataset contains intracranial EEG data recorded during non-REM sleep” (readme).\n2) Task labeling also indicates sleep: tasks = [“sleep”] (tasks field).\n3) No diagnosis/recruitment condition is stated anywhere in provided metadata: the readme only describes iEEG during sleep and funding/authors, and participants_overview only lists counts and “Age range: 1-45” without any clinical group/diagnosis.","paper_abstract_analysis":"No useful paper information. (No abstract provided in the metadata snippet; readme only says “Hatano et al. (in press)”.)","evidence_alignment_check":"Pathology:\n- Metadata says: no explicit disorder; only “intracranial EEG data” and “non-REM sleep” are stated.\n- Few-shot pattern suggests: iEEG datasets are often epilepsy/surgical monitoring, and the epilepsy sleep/HFO example shows sleep EEG can be in epilepsy.\n- Alignment: CONFLICT/INSUFFICIENT—few-shot pattern suggests a likely clinical population, but there is no explicit recruitment diagnosis in metadata; therefore we do not assign a specific pathology.\n\nModality:\n- Metadata says: “recorded during non-REM sleep” and task is “sleep”.\n- Few-shot pattern suggests: sleep recordings use Modality=Sleep (Surrey cEEGrid sleep example).\n- Alignment: ALIGN.\n\nType:\n- Metadata says: the dataset is sleep iEEG (“non-REM sleep”) with task “sleep”, no cognitive task described.\n- Few-shot pattern suggests: sleep-focused datasets use Type=Sleep (Surrey cEEGrid sleep example).\n- Alignment: ALIGN.","decision_summary":"Top-2 candidates and decision per category:\n\nPathology:\n- Candidate 1: Unknown\n  - Evidence: no explicit diagnostic/recruitment statement; metadata only says “intracranial EEG data recorded during non-REM sleep”.\n- Candidate 2: Epilepsy\n  - Evidence: weak contextual inference from “intracranial EEG” (often collected for epilepsy monitoring), plus a loosely similar few-shot clinical sleep EEG example.\n- Decision: Unknown (metadata lacks an explicit pathology; inference not strong enough to assert Epilepsy).\n- Confidence basis: absence of direct quotes naming a diagnosis limits confidence.\n\nModality:\n- Candidate 1: Sleep\n  - Evidence quotes: “recorded during non-REM sleep”; tasks=[“sleep”]. Few-shot support: Surrey sleep example uses Sleep modality.\n- Candidate 2: Resting State\n  - Evidence: sleep could be misconstrued as resting, but metadata explicitly specifies sleep stage.\n- Decision: Sleep.\n\nType:\n- Candidate 1: Sleep\n  - Evidence quotes: “non-REM sleep”; tasks=[“sleep”]. Few-shot support: Surrey sleep example uses Type=Sleep.\n- Candidate 2: Clinical/Intervention\n  - Evidence: intracranial EEG implies clinical monitoring, but the stated purpose is sleep recording rather than an intervention/clinical outcome study in the provided metadata.\n- Decision: Sleep."}},"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":"canonical","author_year":"Hatano2025_part1"}}