{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33de","dataset_id":"ds005398","associated_paper_doi":null,"authors":["Yipeng Zhang","Atsuro Daida","Lawrence Liu","Naoto Kuroda","Yuanyi Ding","Shingo Oana","Tonmoy Monsoor","Chenda Duan","Shaun A. Hussain","Joe X Qiao","Noriko Salamon","Aria Fallah","Myung Shin Sim","Raman Sankar","Richard J. Staba","Jerome Engel Jr.","Eishi Asano","Vwani Roychowdhury","Hiroki Nariai"],"bids_version":"1.7.0","contact_info":["Hiroki Nariai"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds005398.v1.1.1","datatypes":["ieeg"],"demographics":{"subjects_count":185,"ages":[12,8,10,15,5,20,17,6,10,11,17,13,11,14,5,8,19,5,13,9,12,10,11,4,10,16,15,16,10,14,7,28,17,17,30,10,4,9,21,12,28,11,10,16,6,19,12,44,10,15,4,10,12,8,14,14,6,4,8,14,37,19,11,14,17,14,12,11,27,10,19,16,37,13,14,8,15,15,5,15,4,11,18,9,17,7,14,13,19,13,17,9,41,6,12,20,12,19,14,9,3,5,19,13,8,4,8,18,15,19,15,6,20,20,12,14,22,20,14,23,20,6,17,13,9,13,3,19,9,18,17,12,7,10,18,16,25,17,21,25,2,16,12,28,2,16,8,14,13,17,5,10,16,15,7,14,5,16,13,11,10,17,14,8,9,17,12,11,12,11,23,5,13,4,16,8,5,5,7,16,15,8,14,5,13],"age_min":2,"age_max":44,"age_mean":13.35135135135135,"species":null,"sex_distribution":{"o":185},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds005398","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"3d7ff59b4557c4ac6570cc9d0920d64f74cf7bb0bdca07adbebe04152c3a08d7","license":"CC0","n_contributing_labs":null,"name":"Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)","readme":"This dataset was utilized for the publication of the manuscript by Zhang et al. [1]. A subset of the data has been employed in [2], [3], and [4].\nSummary:\nThis data set comprises the de-identified subjects with interictal iEEG recordings with sleep from University of California Los Angels Mattel Children’s Hospital, and Children’s Hospital of Michigan, Detroit.\nSubject-wise information is contained in each folder, including iEEGs collected from 185 subjects during sleep. The channel name and valuables, such as the anatomical label and the resection status, are attached to each folder. The outcome and background information of all the subjects are summarized in ‘paticipant.tsv’ located in the parental directory.\nDerivatives\nThe processed data for HFO detection and classification are shown in the derivatives/folder. The HFO analysis contains detection from two methods: RMS and MNI detectors.\nReferences:\n[1] Zhang Y, Daida A, Liu L, Kuroda N, Ding Y, Oana S, Kanai S, Monsoor T, Duan C, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Sankar R, Staba RJ, Engel J Jr, Asano E, Roychowdhury V, Nariai H. Self-supervised data-driven approach defines pathological high-frequency oscillations in epilepsy. Epilepsia. 2025 Nov;66(11):4434-4450. doi: 10.1111/epi.18545.\n[2] Monsoor T, Kanai S, Daida A, Kuroda N, Sinha P, Oana S, Zhang Y, Liu L, Singh G, Duan C, Sim MS, Fallah A, Speier W, Asano E, Roychowdhury V, Nariai H. Mini-Seizures: Novel Interictal iEEG Biomarker Capturing Synchronization Network Dynamics at the Epileptogenic Zone. medRxiv. 2025 Feb 2:2025.01.31.25321482. doi: 10.1101/2025.01.31.25321482.\n[3] Zhang Y, Lu Q, Monsoor T, Hussain SA, Qiao JX, Salamon N, Fallah A, Sim MS, Asano E, Sankar R, Staba RJ, Engel J Jr, Speier W, Roychowdhury V, Nariai H. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun. 2021 Nov 3;4(1):fcab267. doi: 10.1093/braincomms/fcab267.\n[4] Kuroda N, Sonoda M, Miyakoshi M, Nariai H, Jeong JW, Motoi H, Luat AF, Sood S, Asano E. Objective interictal electrophysiology biomarkers optimize prediction of epilepsy surgery outcome. Brain Commun. 2021 Mar 14;3(2):fcab042. doi: 10.1093/braincomms/fcab042.","recording_modality":["ieeg"],"senior_author":"Hiroki Nariai","sessions":["01"],"size_bytes":109729579196,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["sleep"],"timestamps":{"digested_at":"2026-04-22T12:27:44.086869+00:00","dataset_created_at":"2024-08-04T16:43:46.618Z","dataset_modified_at":"2026-03-24T17:01:03.000Z"},"total_files":185,"storage":{"backend":"s3","base":"s3://openneuro.org/ds005398","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"f04c8666acd6fd85","model":"openai/gpt-5.2","tagged_at":"2026-01-20T17:51:29.120166+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Sleep"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.8,"modality":0.7,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot example is the pediatric epilepsy HFO dataset (\"Dataset of EEG recordings... pediatric patients with epilepsy\"), which is labeled Pathology=Epilepsy and Type=Clinical/Intervention and focuses on HFO detection during sleep EEG. This guides mapping an HFO-biomarker epilepsy cohort to Epilepsy + Clinical/Intervention. A minor convention mismatch is that the few-shot example used Modality=Resting State even though it describes sleep EEG; for the current dataset the metadata explicitly emphasizes sleep recordings, so Sleep is selected for Modality.","metadata_analysis":"Key facts from the provided README:\n1) Clinical cohort and condition: \"interictal iEEG recordings with sleep\" and iEEGs \"from 185 subjects\" from children’s hospitals.\n2) Epilepsy relevance: references explicitly target epilepsy biomarkers/outcomes, e.g., \"Refining epileptogenic high-frequency oscillations\" and \"prediction of epilepsy surgery outcome\".\n3) Clinical/biomarker processing: \"The processed data for HFO detection and classification\" and \"HFO analysis contains detection from two methods\".\nThese lines indicate an epilepsy patient cohort undergoing interictal iEEG (during sleep) with an HFO clinical biomarker/surgery-outcome focus.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"interictal iEEG recordings\" with references to \"epileptogenic high-frequency oscillations\" and \"prediction of epilepsy surgery outcome\".\n- Few-shot suggests: Epilepsy cohorts with HFO detection are labeled Epilepsy.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: \"iEEG recordings with sleep\" and \"185 subjects during sleep\".\n- Few-shot suggests: Similar HFO epilepsy sleep dataset was labeled Resting State (a convention choice).\n- Alignment: PARTIAL CONFLICT (Sleep vs Resting State). Metadata fact (sleep recordings) wins.\n\nType:\n- Metadata says: \"HFO detection and classification\" and references \"prediction of epilepsy surgery outcome\".\n- Few-shot suggests: Epilepsy biomarker datasets and surgery-outcome contexts map to Clinical/Intervention.\n- Alignment: ALIGN.","decision_summary":"Top-2 candidates per category with head-to-head comparison:\n\nPathology:\n1) Epilepsy (WINNER): Supported by \"epileptogenic high-frequency oscillations\" and \"prediction of epilepsy surgery outcome\" plus interictal iEEG from pediatric hospitals.\n2) Other (runner-up): Could apply if condition were unspecified, but epilepsy is explicitly implicated by the cited works.\nAlignment status: Aligns with few-shot epilepsy/HFO example.\n\nModality:\n1) Sleep (WINNER): Explicit in metadata: \"interictal iEEG recordings with sleep\" and \"185 subjects during sleep\".\n2) Resting State (runner-up): Suggested by a labeling convention in the pediatric epilepsy HFO few-shot example.\nAlignment status: Partial conflict; metadata fact (sleep) overrides convention.\n\nType:\n1) Clinical/Intervention (WINNER): Emphasis on clinical biomarker pipeline and outcomes: \"HFO detection and classification\" and \"prediction of epilepsy surgery outcome\".\n2) Sleep (runner-up): Sleep is a recording state here, but not the primary research purpose.\nAlignment status: Aligns with few-shot epilepsy/HFO example.\n\nConfidence justification (quotes/features): Pathology and Type are strongly supported by multiple explicit phrases and cited epilepsy-surgery papers; Modality is directly stated as sleep but has a minor convention-based runner-up (Resting State), reducing confidence slightly."}},"nemar_citation_count":1,"computed_title":"Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)","nchans_counts":[{"val":128,"count":30},{"val":112,"count":20},{"val":104,"count":8},{"val":108,"count":8},{"val":118,"count":6},{"val":124,"count":5},{"val":106,"count":5},{"val":102,"count":5},{"val":132,"count":4},{"val":138,"count":4},{"val":120,"count":4},{"val":100,"count":4},{"val":64,"count":4},{"val":110,"count":3},{"val":122,"count":3},{"val":116,"count":3},{"val":130,"count":3},{"val":114,"count":3},{"val":74,"count":2},{"val":126,"count":2},{"val":150,"count":2},{"val":140,"count":2},{"val":98,"count":2},{"val":58,"count":2},{"val":144,"count":2},{"val":79,"count":2},{"val":86,"count":2},{"val":70,"count":2},{"val":107,"count":2},{"val":77,"count":2},{"val":73,"count":2},{"val":96,"count":2},{"val":76,"count":2},{"val":94,"count":2},{"val":40,"count":1},{"val":63,"count":1},{"val":164,"count":1},{"val":133,"count":1},{"val":45,"count":1},{"val":83,"count":1},{"val":67,"count":1},{"val":69,"count":1},{"val":92,"count":1},{"val":93,"count":1},{"val":149,"count":1},{"val":68,"count":1},{"val":81,"count":1},{"val":80,"count":1},{"val":136,"count":1},{"val":34,"count":1},{"val":72,"count":1},{"val":127,"count":1},{"val":101,"count":1},{"val":95,"count":1},{"val":33,"count":1},{"val":62,"count":1},{"val":109,"count":1},{"val":156,"count":1},{"val":44,"count":1},{"val":84,"count":1},{"val":32,"count":1},{"val":111,"count":1},{"val":56,"count":1},{"val":99,"count":1},{"val":60,"count":1}],"sfreq_counts":[{"val":1000.0,"count":135},{"val":2000.0,"count":49},{"val":200.0,"count":1}],"stats_computed_at":"2026-04-22T23:16:00.309471+00:00","total_duration_s":327560.8355,"author_year":"Zhang2024_Open_Pediatric_Wayne","canonical_name":null}}