{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32b9","dataset_id":"ds003029","associated_paper_doi":null,"authors":["Adam Li","Sara Inati","Kareem Zaghloul","Nathan Crone","William Anderson","Emily Johnson","Iahn Cajigas","Damian Brusko","Jonathan Jagid","Angel Claudio","Andres Kanner","Jennifer Hopp","Stephanie Chen","Jennifer Haagensen","Sridevi Sarma"],"bids_version":"1.4.0","contact_info":["Adam Li"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds003029.v1.0.5","datatypes":["ieeg"],"demographics":{"subjects_count":35,"ages":[30,44,31,43,27,49,59,52,13,28,45,33,39,25,37,39,43,23,32,23,17,31,38,47,36,54,49,36],"age_min":13,"age_max":59,"age_mean":36.535714285714285,"species":null,"sex_distribution":{"f":12,"m":16},"handedness_distribution":{"r":21,"l":2}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds003029","osf_url":null,"github_url":null,"paper_url":null},"funding":["NIH T32 EB003383","NSF GRFP (DGE-1746891)","ARCS Scholarship","Whitaker Fellowship","Chateaubriand Fellowship","NIH R21 NS103113","Coulter Foundation","Maryland Innovation Initiative","US NSF Career Award 1055560","Burroughs Well CASI Award 1007274"],"ingestion_fingerprint":"08cd6bf80f09275c71106338544a6ad77cb14677a182bbd575b91e229480df50","license":"CC0","n_contributing_labs":null,"name":"Epilepsy-iEEG-Multicenter-Dataset","readme":"Fragility Multi-Center Retrospective Study\n------------------------------------------\nThis dataset was updated and prepared for release as part of a manuscript by Bernabei & Li et al. (in preparation). A subset of the data has been featured in [1].\nSummary\n-------------\niEEG and EEG data from 5 centers is organized in our study with a total of 100 subjects. We publish 4 centers' dataset here due to data sharing issues.\nAcquisitions include ECoG and SEEG. Each run specifies a different snapshot of EEG data from that specific subject's session. For seizure sessions, this means that each run is a EEG snapshot around a different seizure event.\nFor additional clinical metadata about each subject, refer to the clinical Excel table in the publication.\nData Availability\n----------------------\nNIH, JHH, UMMC, and UMF agreed to share. Cleveland Clinic did not, so requires an additional DUA.\nAll data, except for Cleveland Clinic was approved by their centers to be de-identified and shared. All data in this dataset have no PHI, or other identifiers associated with patient. In order to access Cleveland Clinic data, please forward all requests to Amber Sours, SOURSA@ccf.org:\nAmber Sours, MPH\nResearch Supervisor | Epilepsy Center\nCleveland Clinic  |  9500 Euclid Ave. S3-399 |  Cleveland, OH 44195\n(216) 444-8638\nYou will need to sign a data use agreement (DUA).\nSourcedata\n----------------\nFor each subject, there was a raw EDF file, which was converted into the BrainVision format with `mne_bids`.\nEach subject with SEEG implantation, also has an Excel table, called `electrode_layout.xlsx`, which outlines where the clinicians marked each electrode anatomically. Note that there is no rigorous atlas applied, so the main points of interest are: `WM`, `GM`, `VENTRICLE`, `CSF`, and `OUT`, which represent white-matter, gray-matter, ventricle, cerebrospinal fluid and outside the brain. WM, Ventricle, CSF and OUT were removed channels from further analysis. These were labeled in the corresponding BIDS `channels.tsv` sidecar file as `status=bad`.\nThe dataset uploaded to `openneuro.org` does not contain the `sourcedata` since there was an extra\nanonymization step that occurred when fully converting to BIDS.\nDerivatives\n----------------\nDerivatives include:\n* fragility analysis\n* frequency analysis\n* graph metrics analysis\n* figures\nThese can be computed by following the following paper:\n[Neural Fragility as an EEG Marker for the Seizure Onset Zone](https://www.biorxiv.org/content/10.1101/862797v3)\nEvents and Descriptions\n-----------------------------------\nWithin each EDF file, there contain event markers that are annotated by clinicians, which may inform you of specific clinical events that are occuring in time, or of when they saw seizures onset and offset (clinical and electrographic).\nDuring a seizure event, specifically event markers may follow this time course:\n\t* eeg onset, or clinical onset - the onset of a seizure that is either marked electrographically, or by clinical behavior. Note that the clinical onset may not always be present, since some seizures manifest without clinical behavioral changes.\n\t* Marker/Mark On - these are usually annotations within some cases, where a health practitioner injects a chemical marker for use in ICTAL SPECT imaging after a seizure occurs. This is commonly done to see which portions of the brain are active metabolically.\n\t* Marker/Mark Off - This is when the ICTAL SPECT stops imaging.\n\t* eeg offset, or clinical offset - this is the offset of the seizure, as determined either electrographically, or by clinical symptoms.\nOther events included may be beneficial for you to understand the time-course of each seizure. Note that ICTAL SPECT occurs in all Cleveland Clinic data. Note that seizure markers are not consistent in their description naming, so one might encode some specific regular-expression rules to consistently capture seizure onset/offset markers across all dataset. In the case of UMMC data, all onset and offset markers were provided by the clinicians on an Excel sheet instead of via the EDF file. So we went in and added the annotations manually to each EDF file.\nSeizure Electrographic and Clinical Onset Annotations\n-----------------------------------------------------------------------------\nFor various datasets, there are seizures present within the dataset. Generally there is only one seizure per EDF file. When seizures are present, they are marked electrographically (and clinically if present) via standard approaches in the epilepsy clinical workflow.\nClinical onset are just manifestation of the seizures with clinical syndromes. Sometimes the maker may not be present.\nSeizure Onset Zone Annotations\n------------------------------\nWhat is actually important in the evaluation of datasets is the clinical annotations of their localization hypotheses of the seizure onset zone.\nThese generally include:\n\t* early onset: the earliest onset electrodes participating in the seizure that clinicians saw\n\t* early/late spread (optional): the electrodes that showed epileptic spread activity after seizure onset. Not all seizures has spread contacts annotated.\nSurgical Zone (Resection or Ablation) Annotations\n-----------------------------------------------------------------------\nFor patients with the post-surgical MRI available, then the segmentation process outlined above tells us which electrodes were within the surgical removed brain region.\nOtherwise, clinicians give us their best estimate, of which electrodes were resected/ablated based on their surgical notes.\nFor surgical patients whose postoperative medical records did not explicitly indicate specific resected or ablated contacts, manual visual inspection was performed to determine the approximate contacts that were located in later resected/ablated tissue. Postoperative T1 MRI scans were compared against post-SEEG implantation CT scans or CURRY coregistrations of preoperative MRI/post SEEG CT scans. Contacts of interest in and around the area of the reported resection were selected individually and the corresponding slice was navigated to on the CT scan or CURRY coregistration. After identifying landmarks of that slice (e.g. skull shape, skull features, shape of prominent brain structures like the ventricles, central sulcus, superior temporal gyrus, etc.), the location of a given contact in relation to these landmarks, and the location of the slice along the axial plane, the corresponding slice in the postoperative MRI scan was navigated to. The resected tissue within the slice was then visually inspected and compared against the distinct landmarks identified in the CT scans, if brain tissue was not present in the corresponding location of the contact, then the contact was marked as resected/ablated. This process was repeated for each contact of interest.\nReferences\n-----------------\n[1] Adam Li, Chester Huynh, Zachary Fitzgerald, Iahn Cajigas, Damian Brusko, Jonathan Jagid, Angel Claudio, Andres Kanner, Jennifer Hopp, Stephanie Chen, Jennifer Haagensen, Emily Johnson, William Anderson, Nathan Crone, Sara Inati, Kareem Zaghloul, Juan Bulacio, Jorge Gonzalez-Martinez, Sridevi V. Sarma. Neural Fragility as an EEG Marker of the Seizure Onset Zone. bioRxiv 862797; doi: https://doi.org/10.1101/862797\n[2] 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[3] 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\n[4] Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8","recording_modality":["ieeg"],"senior_author":"Sridevi Sarma","sessions":["presurgery"],"size_bytes":11083752416,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["ictal"],"timestamps":{"digested_at":"2026-04-22T12:25:30.858507+00:00","dataset_created_at":"2020-07-26T19:33:04.172Z","dataset_modified_at":"2023-11-28T15:39:44.000Z"},"total_files":106,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003029","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"ab9e7180765af48e","model":"openai/gpt-5.2","tagged_at":"2026-01-20T10:13:04.367413+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Other"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.85,"modality":0.65,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot example is the pediatric epilepsy HFO dataset (Pathology=Epilepsy, Modality=Resting State/Sleep-like recording context, Type=Clinical/Intervention). That example establishes the convention that datasets explicitly centered on epilepsy patients and clinically relevant biomarkers/markings (e.g., HFOs, seizure onset zone localization) should be labeled Pathology=Epilepsy and Type=Clinical/Intervention even when there is no experimental cognitive task. This dataset similarly focuses on seizures, seizure onset/offset annotations, and seizure onset zone / surgical zone localization (neural fragility marker), so the same mapping applies. Modality is less directly matched because the few-shot epilepsy dataset is explicitly sleep EEG; here it is peri-ictal monitoring snapshots, so stimulus modality is effectively none/clinical, which conventionally maps better to Modality=Other than to a sensory channel.","metadata_analysis":"Key metadata facts indicating epilepsy/seizure clinical monitoring and surgical localization focus:\n- \"iEEG and EEG data from 5 centers...\" and \"For seizure sessions, this means that each run is a EEG snapshot around a different seizure event.\"\n- \"Within each EDF file, there contain event markers that are annotated by clinicians... when they saw seizures onset and offset (clinical and electrographic).\"\n- \"These generally include: * early onset... electrodes participating in the seizure\" and \"Surgical Zone (Resection or Ablation) Annotations\".\n- Derivatives are explicitly tied to a seizure-onset-zone biomarker paper: \"Derivatives include: * fragility analysis... These can be computed by following the following paper: 'Neural Fragility as an EEG Marker for the Seizure Onset Zone'\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n1) Metadata says: \"For seizure sessions... EEG snapshot around a different seizure event\" and repeatedly discusses \"seizure\" and \"Seizure Onset Zone\".\n2) Few-shot suggests: epilepsy-centered clinical EEG/iEEG datasets should be Pathology=Epilepsy (see pediatric epilepsy HFO example).\n3) ALIGN.\n\nModality:\n1) Metadata says: no sensory stimulus paradigm; instead it is clinical seizure monitoring with clinician annotations (e.g., \"event markers... annotated by clinicians\"; no task/stimuli described).\n2) Few-shot suggests: epilepsy datasets may sometimes be labeled Resting State or Sleep when recording context is explicitly resting/sleep; however that is tied to explicit recording state (e.g., sleep EEG).\n3) PARTIAL CONFLICT/DIFFERENCE: few-shot epilepsy example is sleep EEG (Sleep/Resting State modality), but this dataset is peri-ictal clinical monitoring around seizures without a defined sensory modality.\n4) Metadata wins on facts; choose Modality=Other because there is no dominant sensory input channel described.\n\nType:\n1) Metadata says: clinical localization and intervention relevance: \"Seizure Onset Zone Annotations\" and \"Surgical Zone (Resection or Ablation) Annotations\"; and links to \"Neural Fragility as an EEG Marker for the Seizure Onset Zone\".\n2) Few-shot suggests: clinically focused epilepsy biomarker/localization datasets map to Type=Clinical/Intervention.\n3) ALIGN.","decision_summary":"Top-2 candidates with head-to-head selection:\n\nPathology:\n- Epilepsy (selected): Supported by \"seizure event\", \"seizures onset and offset\", and \"Seizure Onset Zone\" language; also iEEG (ECoG/SEEG) typical of epilepsy presurgical evaluation.\n- Surgery (runner-up): Some subjects may be surgical candidates and there are \"Surgical Zone\" annotations, but recruitment focus is seizure onset localization rather than generic surgical monitoring.\n=> Select Epilepsy. Confidence based on multiple explicit seizure/SOZ quotes.\n\nModality:\n- Other (selected): No explicit stimulus modality; dataset is clinical intracranial/scalp recordings around seizures with clinician annotations (no auditory/visual/tactile task described).\n- Resting State (runner-up): Could be argued as non-task monitoring, but seizures/peri-ictal snapshots are not described as resting-state recordings.\n=> Select Other. Confidence moderate because modality is inferred from absence of stimuli plus clinical context.\n\nType:\n- Clinical/Intervention (selected): Explicit focus on SOZ localization and surgical zone/resection/ablation annotations; biomarker computation \"Neural Fragility... seizure onset zone\".\n- Other (runner-up): Could be considered a methods/biomarker dataset without a classic cognitive construct label, but Clinical/Intervention is better aligned to the stated clinical purpose.\n=> Select Clinical/Intervention. Confidence high due to repeated explicit clinical localization language."}},"nemar_citation_count":19,"computed_title":"Epilepsy-iEEG-Multicenter-Dataset","nchans_counts":[{"val":129,"count":30},{"val":132,"count":8},{"val":123,"count":6},{"val":88,"count":6},{"val":147,"count":6},{"val":135,"count":6},{"val":101,"count":5},{"val":98,"count":4},{"val":91,"count":4},{"val":99,"count":3},{"val":111,"count":3},{"val":81,"count":3},{"val":89,"count":3},{"val":86,"count":3},{"val":110,"count":3},{"val":53,"count":3},{"val":80,"count":3},{"val":60,"count":3},{"val":65,"count":2},{"val":47,"count":1},{"val":216,"count":1}],"sfreq_counts":[{"val":1000.0,"count":56},{"val":999.4121105232217,"count":13},{"val":249.85355222464145,"count":10},{"val":999.9999999999999,"count":9},{"val":499.7071044492829,"count":7},{"val":1000.0000000000001,"count":7},{"val":2000.0000000000002,"count":3},{"val":1024.5997950800408,"count":1}],"stats_computed_at":"2026-04-22T23:16:00.221848+00:00","total_duration_s":29551.732467366317,"author_year":"Li2020","canonical_name":null}}