{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a32f9","dataset_id":"ds003844","associated_paper_doi":null,"authors":["Zweiphenning W.","Demuru M.","van Blooijs D.","Leijten F","Zijlmans M."],"bids_version":"BEP010","contact_info":["Epilab UMCU"],"contributing_labs":null,"data_processed":false,"dataset_doi":"10.18112/openneuro.ds003844.v1.0.1","datatypes":["ieeg"],"demographics":{"subjects_count":6,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":{"f":3,"m":3},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds003844","osf_url":null,"github_url":null,"paper_url":null},"funding":["Epi-Sign Project","Alexandre Suerman Stipendium 2015","Epilepsiefonds #17-07"],"ingestion_fingerprint":"0dcc4c81b43b9d7722cb3dfd1526c0a8cf221738ddb26c55d7b23d89b9980539","license":"CC0","n_contributing_labs":null,"name":"Dataset Clinical Epilepsy iEEG to BIDS -RESPect_intraoperative_iEEG","readme":"Dataset description\nThis dataset is part of a bigger dataset of intracranial EEG (iEEG)  called RESPect (Registry for Epilepsy Surgery Patients), a dataset recorded at the University Medical Center of Utrecht, the Netherlands.\nIt consists of 12 patients: six patients recorded intraoperatively using electrocorticography (acute ECoG), six patients with long-term recordings (3 patients recorded with ECoG and 3 patients recorded with stereo-encephalography SEEG). For a detailed description see (Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A Practical Workflow for Organizing Clinical Intraoperative and Long-Term iEEG data in BIDS”.).\nThis data is organized according to the Brain Imaging Data Structure specification. A community- driven specification for organizing neurophysiology data along with its metadata. For more information on this data specification, see https://bids-specification.readthedocs.io/en/stable/\nEach patient has their own folder (e.g., `sub-RESP0280`) which contains the iEEG recordings data for that patient, as well as the metadata needed to understand the raw data and event timing.\nTwo different implementation of the BIDS structure were done according to the different type of recordings (i.e. intraoperative or long-term)\nIntraoperative ECoG\nSurgery with intraoperative ECoG is composed of three main situations that can be logically grouped into BIDS sessions:\n* Pre-resection sessions, consisting of all recordings (with different configurations of the grid and strips/depth) carried out before the surgeon has started the planned resection.\n* Intermediate sessions, consisting of all subsequent recordings performed before any iterative extension of the resection area.\n* Post-resection sessions, consisting of all the recordings performed after the last resection.\nEach situation is labelled with an increasing number starting from 1, indicative of the period in time respective to the surgical resection and a consecutive letter (starting from A) indicative of the position of the grid and strip/depth for a given session.\nAs an example see patient RESP0280 who had 4 sessions recorded: two pre-resection sessions, one intermediate sessions and one post-resection session. The first session is SITUATION1A consisting of the first recording, then the grid was moved to another position, resulting in SITUATION1B. After that, the surgeon resected part of the brain and then there was another recording(SITUATION2A). Finally the surgeon applied a resection for the last time and the recording after that was defined as SITUATION3A.\nLong-term iEEG\nIn long-term recordings, data that are recorded within one monitoring period are logically grouped in the same BIDS session and stored across runs indicating the day and time point of recording in the monitoring period.\nIf extra electrodes were added/removed during this period, the session was divided into different sessions (e.g. ses-1A and ses-1b).\nWe use the optional run key-value pair to specify the day and the start time of the recording (e.g. run-021315, day 2 after implantation, which is day 1 of the monitoring period, at 13:15).\nThe task key-value pair in long-term iEEG recordings describes the patient’s state during the recording of this file. Different tasks have been defined, such as “rest” when a patient is awake but not doing a specific task, “sleep” when a patient is sleeping the majority of the file, or “SPESclin” when the clinical SPES protocol has been performed in this file. Other task definitions can be found in the annotation syntax (https://github.com/UMCU-EpiLAB/umcuEpi_longterm_ieeg_respect_bids/master/manuals/IFU_annotatingtrc_ECoG).\nLicense\nThis dataset is made available under the Public Domain Dedication and License CC v1.0, whose full text can be found at\nhttps://creativecommons.org/publicdomain/zero/1.0/.\nWe hope that all users will follow the ODC Attribution/Share-Alike Community Norms (http://www.opendatacommons.org/norms/odc-by-sa/);\nin particular, while not legally required, we hope that all users of the data will acknowledge by citing\nDemuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A Practical Workflow for Organizing Clinical Intraoperative and Long-Term iEEG data in BIDS”. Submitted to Neuroinformatics.\nin any publications.\nCode available at: https://github.com/UMCU-EpiLAB.\nAcknowledgements\nWe would like to thank the patients for providing their data for this dataset, the RESPect team of University Medical Center of Utrecht, for the acquisition of the dataset.\nPlease cite Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M, on behalf of the RESPect group. “A Practical Workflow for Organizing Clinical Intraoperative and Long-Term iEEG data in BIDS”. Submitted to Neuroinformatics.\nin any publications.","recording_modality":["ieeg"],"senior_author":"Zijlmans M.","sessions":["SITUATION1A","SITUATION1B","SITUATION1C","SITUATION1D","SITUATION2A","SITUATION2APART1","SITUATION2APART2","SITUATION2B","SITUATION2C","SITUATION2D","SITUATION3A","SITUATION3B","SITUATION3C","SITUATION3D","SITUATION3E","SITUATION3F","SITUATION3G","SITUATION4A"],"size_bytes":2795794610,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["acute"],"timestamps":{"digested_at":"2026-04-22T12:25:44.241952+00:00","dataset_created_at":"2021-10-15T18:46:25.120Z","dataset_modified_at":"2021-10-26T01:07:44.000Z"},"total_files":38,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003844","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"b3945301f69f9afb","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:21:00.068500+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Resting State"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.85,"modality":0.65,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot conventions: (1) “Dataset of EEG recordings of pediatric patients with epilepsy…” labeled Pathology=Epilepsy, Modality=Resting State, Type=Clinical/Intervention. It shows that when participants are explicitly epilepsy patients and the dataset is clinically oriented (biomarkers/clinical workflow), Type maps to Clinical/Intervention even if recordings occur during sleep/rest. (2) “Intraoperative EEG dataset during medianus-tibialis stimulation…” labeled Pathology=Surgery, Modality=Other, Type=Other, illustrating that intraoperative recordings can map to Surgery when surgery itself is the defining recruitment/setting. For the current dataset, metadata explicitly ties participants to epilepsy surgery (both epilepsy and surgery are salient), so we use the epilepsy-specific label for Pathology (diagnosis/recruitment) while keeping the overall study purpose as clinical.","metadata_analysis":"Key explicit facts from the README: (1) Population/clinical context: \"RESPect (Registry for Epilepsy Surgery Patients)\" and \"It consists of 12 patients\". (2) Recording setting and clinical nature: \"six patients recorded intraoperatively using electrocorticography (acute ECoG)\" and \"six patients with long-term recordings ... stereo-encephalography SEEG\". (3) Task/state descriptions indicate largely non-experimental behavioral context: \"Different tasks have been defined, such as 'rest' ... 'sleep' ... or 'SPESclin' when the clinical SPES protocol has been performed\". These point to a clinical iEEG registry around epilepsy surgery/monitoring rather than a controlled sensory paradigm.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says epilepsy surgery patients (\"Registry for Epilepsy Surgery Patients\"); few-shot patterns suggest either Epilepsy (epilepsy clinical dataset) or Surgery (intraoperative setting example). ALIGN partially (both are plausible); choose Epilepsy because diagnosis-based recruitment is explicit and primary.\nModality: Metadata says patient states/tasks like \"rest\", \"sleep\", and clinical stimulation \"SPESclin\"; few-shot suggests Resting State for epilepsy clinical datasets without explicit sensory stimuli, but intraoperative/clinical stimulation settings sometimes map to Other. MIXED/AMBIGUOUS; choose Resting State as the dominant/no-external-stimulus condition, acknowledging some runs may involve clinical electrical stimulation.\nType: Metadata emphasizes clinical registry/workflow and clinical recordings (intraoperative/long-term monitoring) rather than cognitive constructs; few-shot epilepsy dataset maps such clinical epilepsy recordings to Clinical/Intervention. ALIGN; select Clinical/Intervention.","decision_summary":"Top-2 candidates per category:\n\nPathology:\n- Epilepsy (winner): Supported by \"Registry for Epilepsy Surgery Patients\" and all participants being patients undergoing epilepsy-related monitoring/surgery.\n- Surgery (runner-up): Supported by \"recorded intraoperatively\" and resection-related session structure.\nDecision: Epilepsy, because the explicit patient cohort is defined by epilepsy surgery (epilepsy diagnosis is inherent to recruitment), consistent with epilepsy few-shot convention.\n\nModality:\n- Resting State (winner): Supported by task/state definitions including \"rest\" and no described sensory stimulus paradigm.\n- Other (runner-up): Supported by \"SPESclin\" (clinical electrical stimulation), plus intraoperative contexts not fitting classic sensory modalities.\nDecision: Resting State, but with moderate confidence due to mixed recording contexts.\n\nType:\n- Clinical/Intervention (winner): Supported by clinical registry framing and intraoperative/long-term clinical iEEG (\"Registry...\", \"intraoperatively...\", \"clinical SPES protocol\").\n- Resting-state (runner-up): Some recordings are explicitly \"rest\"/\"sleep\", but the dataset’s purpose is broader clinical curation/workflow rather than resting-state neuroscience.\nDecision: Clinical/Intervention, aligning strongly with the epilepsy clinical few-shot example.\n\nConfidence basis: Pathology and Type have multiple explicit metadata quotes; Modality is inferred from mixed task/state descriptions and absence of explicit sensory stimuli."}},"nemar_citation_count":0,"computed_title":"Dataset Clinical Epilepsy iEEG to BIDS -RESPect_intraoperative_iEEG","nchans_counts":[{"val":33,"count":24},{"val":64,"count":9},{"val":32,"count":5}],"sfreq_counts":[{"val":2048.0,"count":33},{"val":256.0,"count":5}],"stats_computed_at":"2026-04-22T23:16:00.306525+00:00","total_duration_s":10004.72802734375,"author_year":"Zweiphenning2021","canonical_name":null,"name_source":"canonical"}}