{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3315","dataset_id":"ds004100","associated_paper_doi":null,"authors":["John M. Bernabei","Adam Li","Andrew Y. Revell","Rachel J. Smith","Kristin M. Gunnarsdottir","Ian Z. Ong","Kathryn A. Davis","Nishant Sinha","Sridevi Sarma","Brian Litt"],"bids_version":"1.4.0","contact_info":["Adam Li","Adam Li","John Bernabei","Ian Ong"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004100.v1.1.3","datatypes":["ieeg"],"demographics":{"subjects_count":57,"ages":[42,21,36,33,25,57,41,56,25,24,35,29,48,39,39,45,36,40,21,43,59,39,36,26,46,47,52,32,37,38,20,47,30,30,31,16,23,17,33,25,32,45,35,42,34,21,26,50,28,24,42,20,28,31,38,25,24,25],"age_min":16,"age_max":59,"age_mean":34.293103448275865,"species":null,"sex_distribution":{"f":31,"m":27},"handedness_distribution":{"r":17,"l":26}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004100","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"7f3acf8de60539348484008bac28e3be154a8d4c5b3bfd5f8ead789056596b77","license":"CC0","n_contributing_labs":null,"name":"HUP iEEG Epilepsy Dataset","readme":"<h1>HUP iEEG dataset</h1>\nThis dataset was prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in Kini & Bernabei et al., Brain (2019) [1], and Bernabei & Sinha et al., Brain (2022) [2].\n<h3>Dataset description</h3>\nThese files contain de-identified patient data collected as part of surgical treatment for drug resistant epilepsy at the Hospital of the University of Pennsylvania. Each of the 58 subjects underwent intracranial EEG with subdural grid, strip, and depth electrodes (ECoG) or purely stereotactically-placed depth electrodes (SEEG). Each patient also underwent subsequent treatment with surgical resection or laser ablation. Electrophysiologic data for both interictal and ictal periods is available, as are electrode localizations in ICBM152 MNI space. Furthermore, clinically-determined seizure onset channels are provided, as are channels which overlap with the resection/ablation zone, which was rigorously determined by segmenting the resection cavity.\n<h3>BIDS Conversion</h3>\nMNE-BIDS was used to convert the dataset into BIDS format.\n<h3>References</h3>\n[1] Kini L.*, Bernabei J.M.*, Mikhail F., Hadar P., Shah P., Khambhati A., Oechsel K., Archer R., Boccanfuso J.A., Conrad E., Stein J., Das S., Kheder A., Lucas T.H., Davis K.A., Bassett D.S., Litt B., Virtual resection predicts surgical outcome for drug resistant epilepsy. Brain, 2019.\n[2] Bernabei J.M.*, Sinha N.*, Arnold T.C., Conrad E., Ong I., Pattnaik A.R., Stein J.M., Shinohara R.T., Lucas T.H., Bassett D.S., Davis K.A., Litt B., Normative intracranial EEG maps epileptogenic tissues in focal epilepsy. Brain, 2022\n[3] Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896\n[4] Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D'Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7","recording_modality":["ieeg"],"senior_author":"Brian Litt","sessions":["presurgery"],"size_bytes":14196260504,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["ictal","interictal"],"timestamps":{"digested_at":"2026-04-22T12:26:06.734316+00:00","dataset_created_at":"2022-04-17T18:17:45.719Z","dataset_modified_at":"2023-03-22T21:26:14.000Z"},"total_files":319,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004100","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"03b47a8eba96fdaa","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:27:48.390630+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Other"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.9,"modality":0.7,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot example by population and clinical context is the pediatric epilepsy dataset (pathology=Epilepsy, modality=Resting State, type=Clinical/Intervention). That example shows the convention that when participants are explicitly recruited for epilepsy and recordings are made in a clinical/monitoring context (sleep EEG for biomarker marking), the Pathology should be Epilepsy and the Type should be Clinical/Intervention. A second relevant convention is the intraoperative stimulation dataset (pathology=Surgery), which illustrates that peri-surgical/clinical recordings without an explicit sensory task may map to non-sensory Modality labels (e.g., Other) rather than Visual/Auditory/etc.","metadata_analysis":"Key facts from the provided README: (1) Clinical recruitment/diagnosis: \"surgical treatment for drug resistant epilepsy\" and \"Each of the 58 subjects underwent intracranial EEG\". (2) Clinical monitoring content: \"Electrophysiologic data for both interictal and ictal periods is available\" and \"clinically-determined seizure onset channels are provided\". (3) Intervention/outcome linkage: \"subsequent treatment with surgical resection or laser ablation\" and channels overlapping \"the resection/ablation zone\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says epilepsy explicitly (\"drug resistant epilepsy\"; \"seizure onset channels\"). Few-shot pattern suggests labeling recruited epilepsy cohorts as Epilepsy (aligns). Modality: Metadata does not describe any external sensory stimulus paradigm; it describes clinical iEEG monitoring (ictal/interictal) and surgical treatment context. Few-shot pattern for clinical epilepsy monitoring sometimes uses Resting State when recordings are task-free (epilepsy sleep HFO example), but here the dataset is not described as a resting-state condition (no eyes-open/closed, no sleep staging as the core paradigm), rather seizure monitoring and surgical mapping—so choosing Other better matches the 'no stimulus modality' convention (partial conflict resolved in favor of metadata specificity/absence of resting-state framing). Type: Metadata emphasizes clinical mapping and treatment (\"surgical resection or laser ablation\"; seizure onset channels; resection zone), aligning with few-shot convention that clinically focused cohorts/maps/outcome prediction are Clinical/Intervention (aligns).","decision_summary":"Pathology top-2: (A) Epilepsy vs (B) Surgery. Evidence for Epilepsy: \"drug resistant epilepsy\", \"ictal periods\", \"seizure onset channels\". Evidence for Surgery: \"surgical treatment\", \"surgical resection or laser ablation\". Head-to-head: recruitment is for epilepsy; surgery is a treatment context not the recruiting diagnosis → select Epilepsy. Modality top-2: (A) Other vs (B) Resting State. Evidence for Other: no described sensory stimuli/task; described as iEEG clinical monitoring with \"interictal and ictal periods\" and surgical mapping variables. Evidence for Resting State: task-free recordings could be loosely construed as resting/monitoring, and a related epilepsy few-shot used Resting State for sleep EEG. Head-to-head: metadata frames seizure monitoring/mapping rather than an explicit resting-state condition → select Other. Type top-2: (A) Clinical/Intervention vs (B) Resting-state. Evidence for Clinical/Intervention: \"surgical treatment\", \"surgical resection or laser ablation\", \"clinically-determined seizure onset channels\", and resection/ablation zone localization. Evidence for Resting-state: presence of interictal (non-task) data. Head-to-head: primary purpose is clinical localization/outcome/intervention linkage → select Clinical/Intervention. Confidence: Pathology high due to multiple explicit epilepsy mentions; Modality moderate because it is inferred from absence of stimulus/task description; Type high due to multiple explicit clinical intervention/localization statements."}},"nemar_citation_count":21,"computed_title":"HUP iEEG Epilepsy Dataset","nchans_counts":[{"val":122,"count":21},{"val":128,"count":18},{"val":118,"count":17},{"val":172,"count":15},{"val":126,"count":14},{"val":104,"count":13},{"val":82,"count":12},{"val":180,"count":12},{"val":127,"count":12},{"val":96,"count":12},{"val":149,"count":7},{"val":74,"count":7},{"val":102,"count":7},{"val":117,"count":7},{"val":121,"count":7},{"val":109,"count":7},{"val":80,"count":7},{"val":120,"count":7},{"val":92,"count":7},{"val":190,"count":7},{"val":136,"count":7},{"val":108,"count":7},{"val":174,"count":7},{"val":163,"count":6},{"val":98,"count":6},{"val":100,"count":5},{"val":162,"count":5},{"val":186,"count":5},{"val":63,"count":5},{"val":59,"count":5},{"val":88,"count":5},{"val":71,"count":5},{"val":116,"count":5},{"val":52,"count":5},{"val":164,"count":5},{"val":90,"count":4},{"val":61,"count":4},{"val":105,"count":4},{"val":85,"count":3},{"val":94,"count":2},{"val":192,"count":2},{"val":232,"count":1}],"sfreq_counts":[{"val":512.0,"count":165},{"val":1024.0,"count":78},{"val":500.0,"count":69},{"val":256.0,"count":7}],"stats_computed_at":"2026-04-22T23:16:00.306956+00:00","total_duration_s":92584.43621875,"canonical_name":null,"name_confidence":0.63,"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":"Bernabei2022"}}