{"dataset":{"id":"60748","dataset_id":"on005398","name":"Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)","description":"This dataset contains de-identified intracranial EEG (iEEG) recordings during sleep from 185 pediatric epilepsy patients collected at UCLA Mattel Children's Hospital and Children's Hospital of Michigan, Detroit. It includes channel-level anatomical labels, resection status, and outcome information, along with derivatives for high-frequency oscillation (HFO) detection and classification using RMS and MNI detectors. The dataset supports research on interictal iEEG biomarkers for predicting epilepsy surgery outcomes.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on005398","concept_doi":"10.82901/nemar.on005398","latest_version_doi":"10.82901/nemar.on005398.v1.0.0","created_at":"2026-06-26 23:31:42","updated_at":"2026-08-19 01:45:33","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"validated\",\n  \"title\": \"Open iEEG Dataset (Pediatric iEEG, Wayne State University and UCLA)\",\n  \"description\": \"This dataset contains de-identified intracranial EEG (iEEG) recordings during sleep from 185 pediatric epilepsy patients collected at UCLA Mattel Children's Hospital and Children's Hospital of Michigan, Detroit. It includes channel-level anatomical labels, resection status, and outcome information, along with derivatives for high-frequency oscillation (HFO) detection and classification using RMS and MNI detectors. The dataset supports research on interictal iEEG biomarkers for predicting epilepsy surgery outcomes.\",\n  \"methods_description\": \"Interictal iEEG recordings were collected during sleep from pediatric epilepsy patients. Subject-wise data includes channel names, anatomical labels, and resection status. HFO detection and classification derivatives were generated using RMS and MNI detection methods, with additional bipolar iEEG data processed for time-frequency analysis.\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"Yipeng Zhang\": {\n      \"orcid\": \"0000-0003-2869-4692\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Electrical and Computer Engineering University of California, Los Angeles (UCLA)  Los Angeles California USA\"\n        }\n      ]\n    },\n    \"Atsuro Daida\": {\n      \"orcid\": \"0000-0003-1350-7700\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Pediatrics, Division of Pediatric Neurology, David Geffen School of Medicine at the University of California, Los Angeles, California, USA\"\n        }\n      ]\n    },\n    \"Lawrence Liu\": {},\n    \"Naoto Kuroda\": {\n      \"orcid\": \"0000-0003-1977-4893\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Pediatrics and Neurology, Children's Hospital of Michigan Wayne State University School of Medicine  Detroit Michigan USA\"\n        }\n      ]\n    },\n    \"Yuanyi Ding\": {},\n    \"Shingo Oana\": {},\n    \"Tonmoy Monsoor\": {},\n    \"Chenda Duan\": {},\n    \"Shaun A. Hussain\": {\n      \"orcid\": \"0000-0001-6947-8852\",\n      \"affiliations\": [\n        {\n          \"name\": \"Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital David Geffen School of Medicine at UCLA  Los Angeles California USA\"\n        },\n        {\n          \"name\": \"UCLA Children's Discovery and Innovation Institute  Los Angeles California USA\"\n        }\n      ]\n    },\n    \"Joe X Qiao\": {},\n    \"Noriko Salamon\": {},\n    \"Aria Fallah\": {\n      \"orcid\": \"0000-0002-9703-0964\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Neurosurgery, UCLA Medical Center David Geffen School of Medicine  Los Angeles California USA\"\n        }\n      ]\n    },\n    \"Myung Shin Sim\": {},\n    \"Raman Sankar\": {},\n    \"Richard J. Staba\": {},\n    \"Jerome Engel Jr.\": {},\n    \"Eishi Asano\": {\n      \"orcid\": \"0000-0001-8391-4067\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Pediatrics and Neurology, Children's Hospital of Michigan Wayne State University School of Medicine  Detroit Michigan USA\"\n        }\n      ]\n    },\n    \"Vwani Roychowdhury\": {\n      \"orcid\": \"0000-0003-0832-6489\",\n      \"affiliations\": [\n        {\n          \"name\": \"Department of Electrical and Computer Engineering University of California, Los Angeles (UCLA)  Los Angeles California USA\"\n        }\n      ]\n    },\n    \"Hiroki Nariai\": {\n      \"orcid\": \"0000-0002-8318-2924\",\n      \"affiliations\": [\n        {\n          \"name\": \"Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital David Geffen School of Medicine at UCLA  Los Angeles California USA\"\n        },\n        {\n          \"name\": \"UCLA Children's Discovery and Innovation Institute  Los Angeles California USA\"\n        }\n      ]\n    }\n  },\n  \"keywords\": [\n    {\n      \"term\": \"Electroencephalography\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004569\"\n    },\n    {\n      \"term\": \"Epilepsy\",\n      \"subject_scheme\": \"MeSH\",\n      \"value_uri\": \"http://id.nlm.nih.gov/mesh/D004827\"\n    },\n    {\n      \"term\": \"high-frequency oscillations\"\n    },\n    {\n      \"term\": \"intracranial EEG\"\n    },\n    {\n      \"term\": \"pediatric epilepsy\"\n    },\n    {\n      \"term\": \"epilepsy surgery\"\n    },\n    {\n      \"term\": \"sleep\"\n    },\n    {\n      \"term\": \"HFO detection\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on005398\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1111/epi.18545\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.1101/2025.01.31.25321482\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.1093/braincomms/fcab267\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.1093/braincomms/fcab042\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsReferencedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds005398.v1.1.3\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.64898/2026.06.12.26355482\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsSupplementTo\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataset/on005398\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"National Institutes of Health\",\n      \"award_number\": \"\"\n    },\n    {\n      \"funder_name\": \"National Institutes of Health\",\n      \"award_number\": \"Not specified\"\n    }\n  ],\n  \"resource_type_general\": \"Dataset\",\n  \"resource_type_specific\": \"iEEG Dataset\",\n  \"modalities\": [\n    \"ieeg\"\n  ],\n  \"sizes\": [\n    \"176.8 GB (537 files)\"\n  ],\n  \"formats\": [\n    \".csv\",\n    \".edf\",\n    \".json\",\n    \".md\",\n    \".tsv\",\n    \".yml\"\n  ],\n  \"source_hash\": \"1717b7e1077c668f8c14e76c6af0db5c9420e98792634218ef2fa8f59cdead8f\"\n}","last_activity_at":"2026-06-26 23:31:42","source":"openneuro","source_id":"ds005398","subject_count":185,"modalities":"ieeg","age_min":2,"age_max":44,"file_size":176779910613,"total_files":1656,"tasks":"sleep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":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","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005398-blue)](https://doi.org/10.82901/nemar.on005398)\n\nThis 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], [4], and [5].\n\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.\n\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. The bipolar iEEG data used for [5] is also uploaded, and the credit for creating such dataset is given to the authors of the manuscript. \n\nReferences: \n\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\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\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\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.\n\n[5]  Blanca Romero Milà, Nathan Phi Hoang, Marco Pinto-Orellana, Atsuro Daida, Sotaro Kanai, Naoto Kuroda, Shaun A. Hussain, Daniel W. Shrey, Eishi Asano, Hiroki Nariai, Beth A. Lopour. Discovering Novel intracranial EEG Biomarkers of Seizure Generating Tissue through Time-Frequency Analysis. medRxiv. doi: https://doi.org/10.64898/2026.06.12.26355482. Posted June 22, 2026.","bids_version":"1.7.0","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-08-16 21:46:55","zarr_store_count":185,"zarr_index_etag":"59a1c8a5cbe44e3b66c3e10225792d27","zarr_source_commit":"71438646809690945a1586368ae3b2be11479bfb","archive_status":null,"archive_size":null,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":"dataset 164.6 GB exceeds 100.0 GB archive limit; use direct download","zarr_errors":167,"zarr_failure_count":134,"zarr_deterministic":0,"zarr_failed_at":"2026-08-16 21:46:55","num_dataset_citations":3,"num_datapaper_citations":11,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":176776540290,"data_complete":1,"withdrawn_at":null,"withdrawn_reason":null,"archive_complete":null,"archive_absent_files":null,"archive_declared_files":null,"zarr_pool_breaks":null,"total_recording_duration":329470,"recording_duration_min":303,"recording_duration_max":7201,"recording_count":319,"recordings_unavailable":134,"recordings_measured":185,"channel_count_min":32,"channel_count_max":164,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-08-19 01:45:20\",\"metadata_updated_at\":\"2026-08-19 01:45:32\",\"archive_checked_at\":\"2026-06-26 23:44:39\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-26 23:45:09\",\"citations_updated_at\":\"2026-09-08 03:00:49\",\"channel_montage_checked_at\":\"2026-06-28 23:42:16\",\"hed_checked_at\":\"2026-06-30 05:16:44\",\"data_checked_at\":\"2026-08-20 03:00:20\",\"availability_report_at\":\"2026-07-23 01:24:13\",\"recording_stats_at\":\"2026-09-02 11:33:17\",\"signal_defaults_at\":\"2026-09-02 12:29:09\"}","participants":185,"num_citations":14,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"165 GB","zarr_data_failures":{"count":134,"detail_ref":"zarr/index.json","compacted_by":"migration_0074"},"zarr_index_url":"https://zarr.nemar.org/on005398/zarr/index.json","attestation_deposit_type":null,"attestation_key_status":null,"attestation_deidentified":null,"attestation_no_duplicate":null,"attestation_upstream_source":null,"attestation_accepted_at":null}}