{"dataset":{"id":"54510","dataset_id":"on003848","name":"Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG","description":"This dataset comprises intracranial EEG (iEEG) recordings from 12 epilepsy surgery patients enrolled in the RESPect (Registry for Epilepsy Surgery Patients) study at the University Medical Center of Utrecht. The collection includes intraoperative electrocorticography (ECoG) recordings from six patients and long-term iEEG monitoring data from six patients (three with ECoG and three with stereo-encephalography). All data are organized according to the Brain Imaging Data Structure (BIDS) specification to facilitate standardized access and analysis of clinical neurophysiology data. This dataset is derived from the larger RESPect iEEG collection and has been curated for BIDS compliance.","owner_user_id":15,"status":"active","github_repo":"nemarDatasets/on003848","concept_doi":"10.82901/nemar.on003848","latest_version_doi":"10.82901/nemar.on003848.v1.0.0","created_at":"2026-06-22 19:31:13","updated_at":"2026-07-10 22:21:50","zenodo_concept_id":null,"is_sandbox":0,"visibility":"public","ezid_status":"public","enrichment_json":"{\n  \"version\": \"2.0\",\n  \"pipeline_stage\": \"enriched\",\n  \"title\": \"Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG\",\n  \"description\": \"This dataset comprises intracranial EEG (iEEG) recordings from 12 epilepsy surgery patients enrolled in the RESPect (Registry for Epilepsy Surgery Patients) study at the University Medical Center of Utrecht. The collection includes intraoperative electrocorticography (ECoG) recordings from six patients and long-term iEEG monitoring data from six patients (three with ECoG and three with stereo-encephalography). All data are organized according to the Brain Imaging Data Structure (BIDS) specification to facilitate standardized access and analysis of clinical neurophysiology data. This dataset is derived from the larger RESPect iEEG collection and has been curated for BIDS compliance.\",\n  \"methods_description\": \"Intracranial EEG recordings were acquired during epilepsy surgery procedures (intraoperative ECoG) and during long-term clinical monitoring periods. Intraoperative recordings were organized into sessions corresponding to pre-resection, intermediate, and post-resection surgical phases. Long-term recordings were grouped by monitoring period with runs indicating specific days and times of acquisition. Electrode configurations included grids, strips, and depth electrodes for ECoG, and depth electrodes for stereo-encephalography (SEEG). Data were converted to BIDS format following the practical workflow described in Demuru et al. (2021).\",\n  \"license\": \"CC0\",\n  \"dataset_type\": \"raw\",\n  \"authors\": {\n    \"van Blooijs D.\": {},\n    \"Demuru M.\": {},\n    \"Zweiphenning W\": {},\n    \"Hermes D.\": {},\n    \"Leijten F.\": {},\n    \"Zijlmans M.\": {}\n  },\n  \"keywords\": [\n    {\n      \"term\": \"intracranial EEG\"\n    },\n    {\n      \"term\": \"electrocorticography\"\n    },\n    {\n      \"term\": \"stereo-encephalography\"\n    },\n    {\n      \"term\": \"epilepsy surgery\"\n    },\n    {\n      \"term\": \"clinical neurophysiology\"\n    },\n    {\n      \"term\": \"BIDS\"\n    },\n    {\n      \"term\": \"RESPect\"\n    },\n    {\n      \"term\": \"electrode grids\"\n    },\n    {\n      \"term\": \"depth electrodes\"\n    },\n    {\n      \"term\": \"intraoperative recording\"\n    },\n    {\n      \"term\": \"long-term monitoring\"\n    },\n    {\n      \"term\": \"surgical epilepsy\"\n    }\n  ],\n  \"related_identifiers\": [\n    {\n      \"identifier\": \"https://github.com/nemarDatasets/on003848\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"https://nemar.org/dataexplorer/detail?dataset_id=on003848\",\n      \"identifier_type\": \"URL\",\n      \"relation_type\": \"IsDescribedBy\"\n    },\n    {\n      \"identifier\": \"10.18112/openneuro.ds003848.v1.0.3\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDerivedFrom\"\n    },\n    {\n      \"identifier\": \"10.1038/s41597-021-00862-6\",\n      \"identifier_type\": \"DOI\",\n      \"relation_type\": \"IsDescribedBy\"\n    }\n  ],\n  \"funding_references\": [\n    {\n      \"funder_name\": \"Epi-Sign Project\"\n    },\n    {\n      \"funder_name\": \"EpilepsieNL\",\n      \"award_number\": \"#17-07\"\n    },\n    {\n      \"funder_name\": \"Alexandre Suerman Stipendium\",\n      \"award_number\": \"2015\"\n    },\n    {\n      \"funder_name\": \"EpilepsieNL\",\n      \"award_number\": \"17-07\"\n    }\n  ],\n  \"resource_type_specific\": \"Structural MRI Dataset\",\n  \"modalities\": [\n    \"anat\",\n    \"ieeg\"\n  ],\n  \"sizes\": [\n    \"106.3 GB (1984 files)\"\n  ],\n  \"formats\": [\n    \".H\",\n    \".K\",\n    \".TRC\",\n    \".annot\",\n    \".area\",\n    \".avg_curv\",\n    \".bak\",\n    \".cmd\",\n    \".crv\",\n    \".ctab\",\n    \".curv\",\n    \".dat\",\n    \".defect_borders\",\n    \".defect_chull\",\n    \".defect_labels\",\n    \".done\",\n    \".eeg\",\n    \".env\",\n    \".gii\",\n    \".inflated\",\n    \".jacobian_white\",\n    \".json\",\n    \".label\",\n    \".local-copy\",\n    \".log\",\n    \".lta\",\n    \".m3z\",\n    \".md\",\n    \".mgh\",\n    \".mgz\",\n    \".mid\",\n    \".nii\",\n    \".nofix\",\n    \".old\",\n    \".orig\",\n    \".pial\",\n    \".preaparc\",\n    \".reg\",\n    \".smoothwm\",\n    \".sphere\",\n    \".stats\",\n    \".sulc\",\n    \".thickness\",\n    \".touch\",\n    \".tsv\",\n    \".txt\",\n    \".vhdr\",\n    \".vmrk\",\n    \".volume\",\n    \".white\",\n    \".xfm\",\n    \".yml\"\n  ],\n  \"source_hash\": \"349b3876eb3daf64ba555e5d91e0731de5bd4356cd5df79311e34a68878885a2\"\n}","last_activity_at":"2026-06-22 19:31:13","source":"openneuro","source_id":"ds003848","subject_count":6,"modalities":"anat,ieeg","age_min":14,"age_max":46,"file_size":106583713235,"total_files":2388,"tasks":"CHOCS1,Rest,SPESclin,Sleep,slawtrans,sleep","metadata_columns_error":null,"staleness_warn_stage":null,"staleness_admin_notified_at":null,"authors":"van Blooijs D., Demuru M., Zweiphenning W, Hermes D., Leijten F., Zijlmans M.","license":"CC0","readme":"[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on003848-blue)](https://doi.org/10.82901/nemar.on003848)\n\nDataset 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“€, submitted to NeuroInformatics in 2020.\n\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/ \n\n\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.\n\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\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\n* Intermediate sessions, consisting of all subsequent recordings performed before any iterative extension of the resection area.\n\n* Post-resection sessions, consisting of all the recordings performed after the last resection.\n\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.  \n\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).\n\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 in 2020, in any publications.\n\nCode available at: https://github.com/UMCU-EpiLAB.\n\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 in 2020, in any publications.","bids_version":"Brain Imaging Data Structure Specification v1.6.0","sessions_count":1,"publish_date":null,"embedding_dirty":0,"license_tier":"public","zarr_status":"ready","zarr_converted_at":"2026-08-04 15:12:20","zarr_store_count":16,"zarr_index_etag":"480658dde4f13ab12248048d9ca5b87d","zarr_source_commit":"4771e2e9702a2d62db49f3f1362a67ea5b835643","archive_status":"ready","archive_size":72449075325,"archive_retry_count":0,"records_status":"ready","archive_skip_reason":null,"zarr_errors":6,"zarr_failure_count":0,"zarr_deterministic":0,"zarr_failed_at":"2026-08-04 15:12:20","num_dataset_citations":1,"num_datapaper_citations":0,"n_channels":null,"electrode_system":null,"has_hed":0,"hed_version":null,"is_exemplar":0,"bytes_present":106329313902,"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":51392.00074999999,"recording_duration_min":1063.281,"recording_duration_max":3840.125,"recording_count":16,"recordings_unavailable":0,"recordings_measured":16,"channel_count_min":68,"channel_count_max":133,"sampling_frequency":null,"power_line_frequency":null,"eeg_reference":null,"placement_scheme":null,"sweep_stamps":"{\"enrichment_updated_at\":\"2026-06-22 19:46:12\",\"metadata_updated_at\":\"2026-06-22 19:46:19\",\"archive_checked_at\":\"2026-06-22 20:38:55\",\"zarr_checked_at\":null,\"records_checked_at\":\"2026-06-22 19:47:10\",\"citations_updated_at\":\"2026-09-08 03:00:52\",\"channel_montage_checked_at\":\"2026-06-28 23:16:41\",\"hed_checked_at\":\"2026-06-30 04:48:49\",\"data_checked_at\":\"2026-08-04 03:00:12\",\"availability_report_at\":\"2026-07-23 01:15:42\",\"recording_stats_at\":\"2026-09-02 11:32:35\",\"signal_defaults_at\":\"2026-09-02 12:04:27\"}","participants":6,"num_citations":1,"latest_version":"v1.0.0","zarr_verify_status":null,"zarr_verified_at":null,"owner_username":"nemarAdmin","owner_github":"nemarAdmin","file_size_formatted":"99.26 GB","zarr_data_failures":null,"zarr_index_url":"https://zarr.nemar.org/on003848/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}}