{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32cd","dataset_id":"ds003498","associated_paper_doi":null,"authors":["Fedele T","Krayenbühl N","Hilfiker P","Adam Li","Sarnthein J."],"bids_version":"1.4.0","contact_info":["Johannes Sarnthein","Adam Li"],"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds003498.v1.0.1","datatypes":["ieeg"],"demographics":{"subjects_count":20,"ages":[33,20,20,40,48,25,21,52,37,36,49,17,46,31,17,30,40,38,17],"age_min":17,"age_max":52,"age_mean":32.473684210526315,"species":null,"sex_distribution":{"m":13,"f":6},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds003498","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"72524f1249a509581ca4e4cd46a6565847ffdd9476d214a87753f8a7e110ff41","license":"CC0","n_contributing_labs":null,"name":"interictal iEEG during slow-wave sleep with HFO markings","readme":"Zurich iEEG HFO Dataset\n====================\nThis dataset was obtained from the publication [1].\nThere are 20 subjects with HFO events. We converted the dataset into BIDS format.\nThe original uploader: adam2392 obtained explicit permission from the authors of the dataset to upload this to openneuro. Adam worked on an open-source Python implementation of HFO detection algorithms, and uses this dataset in validation. Even though the publication involves a ``Morphology`` HFO detector, we have implemented our interpretation of the RMS, LineLength and Hilbert detectors in the [mne-hfo repository] (https://github.com/adam2392/mne-hfo) [2].For more information, visit: https://github.com/adam2392/mne-hfo.\n# Note from the paper\n\"We excluded all electrode contacts where electrical stimulation evoked motor or language responses (Table S1).\nIn TLE patients, we included only the 3 most mesial bipolar channels\".\nBIDS Conversion\n-----------------------\nMNE-BIDS was used to convert the dataset into BIDS format. The code inside `code/` was used to generate the\ndata.\nHFO Events From Original Paper\n---------------------------------------------\nThe HFO events from the original paper that were validated and detected are stored in the `*events.tsv` file per dataset run. The format is similar to ``mne-hfo`` and can be easily read in using ``mne-bids`` and/or ``mne-python``.\nEach row in the events.tsv file corresponds to a HFO detected in the original source dataset. The ``trial_type`` column stores the information pertaining type of HFO (e.g. ``ripple``, ``fr`` for fast ripple, or ``frandr`` for fast ripple and ripple). The channel name (possibly in bipolar reference) is `\"-\"` character delimited and appended to the type of HFO with a `\"_\"` separating. For example: ``<hfo_type>_<channel_name>`` is the form.\nReference Dataset\n--------------------------\nThe following website was where the original data was downloaded.\nhttp://crcns.org/data-sets/methods/ieeg-1\nReferences\n---------------\n[1] Fedele T, Burnos S, Boran E, Krayenbühl N, Hilfiker P, Grunwald T, Sarnthein J.\nResection of high frequency oscillations predicts seizure outcome in the individual patient.\nScientific Reports. 2017;7(1):13836.\nhttps://www.nature.com/articles/s41598-017-13064-1\ndoi:10.1038/s41598-017-13064-1\n[2] Dataset meta analysis with mne-hfo. 10.5281/zenodo.4485036\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":"Sarnthein J.","sessions":["interictalsleep"],"size_bytes":48030875385,"source":"openneuro","study_design":null,"study_domain":null,"tasks":[],"timestamps":{"digested_at":"2026-04-22T12:25:33.384414+00:00","dataset_created_at":"2021-02-01T13:44:55.139Z","dataset_modified_at":"2023-09-26T00:54:04.000Z"},"total_files":385,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003498","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"f8da991724e28762","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:16:28.325651+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Resting State"],"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... HFO\"), labeled Pathology=Epilepsy, Modality=Resting State, Type=Clinical/Intervention. The current dataset likewise centers on HFO detection in epilepsy patients and relates HFOs to surgical seizure outcome, so the same mapping convention (HFO biomarker work in epilepsy cohorts -> Clinical/Intervention; no explicit stimulus/task -> Resting State) applies. A secondary reference is the intraoperative SEP dataset labeled Pathology=Surgery, showing that when stimulation/surgery context dominates, 'Surgery' can be used; here, however, the cohort is explicitly epilepsy (TLE), so Epilepsy is preferred for Pathology.","metadata_analysis":"Key quoted metadata facts:\n1) Clinical population / condition: \"In TLE patients, we included only the 3 most mesial bipolar channels\".\n2) Clinical intervention/outcome focus: \"Resection of high frequency oscillations predicts seizure outcome in the individual patient.\" \n3) Biomarker/feature labeling: \"There are 20 subjects with HFO events.\" and \"Each row in the events.tsv file corresponds to a HFO detected in the original source dataset.\" \n4) Exclusion based on functional mapping suggests intracranial clinical context: \"We excluded all electrode contacts where electrical stimulation evoked motor or language responses\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"In TLE patients...\" and the cited paper title concerns seizure outcome, indicating epilepsy (temporal lobe epilepsy).\n- Few-shot pattern suggests: Epilepsy for HFO datasets in epilepsy cohorts (pediatric epilepsy HFO example).\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: no explicit sensory stimulus/task is described; content is HFO detection and clinical electrode selection (e.g., \"We excluded... electrical stimulation... responses\").\n- Few-shot pattern suggests: when no external stimuli/task are described and recordings are analyzed for biomarkers, label Modality=Resting State (epilepsy HFO example).\n- Alignment: ALIGN (but relies on absence of task description rather than an explicit 'resting-state' statement).\n\nType:\n- Metadata says: focus on clinical biomarker linked to surgery outcome: \"Resection of high frequency oscillations predicts seizure outcome\".\n- Few-shot pattern suggests: biomarker/clinical cohort emphasis -> Type=Clinical/Intervention (epilepsy HFO example; dementia resting dataset example).\n- Alignment: ALIGN.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n1) Epilepsy (selected) — Evidence: \"In TLE patients...\"; seizure-outcome focus in cited paper title \"Resection of high frequency oscillations predicts seizure outcome\".\n2) Surgery — Evidence: explicit \"Resection\" and electrode selection/exclusion based on stimulation mapping, suggesting surgical epilepsy candidates.\nHead-to-head: Recruitment is defined by epilepsy (TLE) rather than surgery itself; surgery is context/intervention. => Epilepsy.\nConfidence=0.8 (2 strong quotes: TLE patients; seizure/resection outcome title, plus strong few-shot analog).\n\nModality:\n1) Resting State (selected) — Evidence: no sensory stimuli described; dataset centers on detected HFO events rather than task-evoked responses; matches few-shot epilepsy HFO convention.\n2) Other — Evidence: presence of electrical stimulation mapping context could imply a non-rest paradigm, but stimulation is mentioned as an exclusion criterion rather than a primary stimulus protocol.\nHead-to-head: Lack of a described stimulus/task supports Resting State more than Other. \nConfidence=0.7 (no explicit 'resting-state' quote; inference from absence of task + few-shot analog).\n\nType:\n1) Clinical/Intervention (selected) — Evidence: \"Resection... predicts seizure outcome\" directly frames a clinical prognostic/intervention-outcome goal; epilepsy biomarker detection.\n2) Other — Evidence: could be viewed as methods validation for HFO detectors (\"validation\" and algorithm implementations), but still in a clinical outcome setting.\nHead-to-head: Clinical seizure outcome prediction via resection is the primary framing. \nConfidence=0.8 (clear clinical-outcome quote + strong few-shot analog)."}},"nemar_citation_count":3,"computed_title":"interictal iEEG during slow-wave sleep with HFO markings","nchans_counts":[{"val":64,"count":146},{"val":40,"count":73},{"val":42,"count":43},{"val":74,"count":35},{"val":16,"count":29},{"val":50,"count":28},{"val":48,"count":13},{"val":52,"count":13},{"val":30,"count":5}],"sfreq_counts":[{"val":2000.0,"count":385}],"stats_computed_at":"2026-04-22T23:16:00.222110+00:00","total_duration_s":116438.80750000001,"author_year":"Fedele2021","canonical_name":null}}