{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a33a3","dataset_id":"ds004944","associated_paper_doi":null,"authors":["Filippo Costa","Niklaus Krayenbühl","Georgia Ramantani","Ece Boran","Kristina König","Johannes Sarnthein"],"bids_version":"1.4.0","contact_info":["Filippo Costa","Johannes Sarnthein"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds004944.v1.1.0","datatypes":["ieeg"],"demographics":{"subjects_count":22,"ages":[15,7,17,22,20,3,34,23,1,15,36,38,42,7,17,67,3,4,17,4,41,5],"age_min":1,"age_max":67,"age_mean":19.90909090909091,"species":null,"sex_distribution":{"f":12,"m":10},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004944","osf_url":null,"github_url":null,"paper_url":null},"funding":["Swiss National Science Foundation, SNSF 204651"],"ingestion_fingerprint":"6cd41ae0d55814bdafd3728a8f6d374b5dd08aca019cd1dff565198dfb9c7c07","license":"CC0","n_contributing_labs":null,"name":"Dataset of BCI2000-compatible intraoperative ECoG with neuromorphic encoding","readme":"## Overview\nThis dataset comprises recordings of intraoperative Electrocorticography (ECoG) from 22 patients undergoing resective epilepsy surgery.\nFor each patient, the dataset is organized into pre-resection recording (referred to as SITUATION1A) and post-resection recording (referred to as SITUATION2A).\nWe provide raw ECoG recordings for each patient and a derivative folder that contains all the main processing stages\nperformed with our neuromorphic processing pipeline: [https://doi.org/10.1038/s41467-024-47495-y](https://doi.org/10.1038/s41467-024-47495-y).\nThe pipeline preprocesses ECoG recordings in real-time and performs Asynchronous Delta Modulator (ADM) encoding with a custom BCI2000 module.\nThe ADM-encoded data are processed by a hardware Spiking Neural Network (SNN). The SNN-encoded data are then used to detect epileptiform patterns in the ECoG.\nThe code to perform preprocessing and ADM encoding in BCI2000, together with the code to detect epileptiform patterns from SNN-encoded data, are provided at [https://github.com/CostaFilippo/BCI2000_DYNAP-SE.git](https://github.com/CostaFilippo/BCI2000_DYNAP-SE.git).\nIn a previous publication, this dataset has been analyzed with a offline software algorithm: [https://doi.org/10.1016/j.clinph.2019.07.008](https://doi.org/10.1016/j.clinph.2019.07.008).\nThe annotations of the epileptiform patterns detected with the offline approach are provided at: sub-\\*/ses-SITUATION\\*/sub-\\*_ses-SITUATION\\*_task-acute_events.tsv.\nThe annotations of the epileptiform patterns detected with the online neuromorphic processing are provided at derivative/sub-\\*/ses-SITUATION\\*/sub-\\*_ses-SITUATION*_task-EV.csv.\n## Dataset Structure\nThe derivative folder is structured as follows:\n - sub-*\n  - ses-SITUATION1A\n    - *task-BCI.dat\n    - *task-ADM.csv\n    - *task-SNN.csv\n    - *task-EV.csv\n  - ses-SITUATION2A\n    - *task-BCI.dat\n    - *task-ADM.csv\n    - *task-SNN.csv\n    - *task-EV.csv\n### File Descriptions\n- derivative\n - **task-BCI.dat:* BCI2000-compatible file containing the ECoG recording.\n - **task-ADM.csv:* ADM encoding of the ECoG recording.\n - **task-SNN.csv:* SNN encoding of the ECoG recording.\n - **task-EV.cvs:*  Annotations of the detected epileptiform patterns in the ECoG recording.\n## Data Formats\nDetails of the neuromorphic processing pipeline can be found at [https://doi.org/10.1038/s41467-024-47495-y](https://doi.org/10.1038/s41467-024-47495-y).\n#### task-BCI.dat\nThe BCI2000-compatible file contains the raw ECoG recording.\nIt can be streamed in real-time using the 'FilePlayback' BCI2000 module.\n#### task-ADM.csv\nThe ADM file is formatted as follows:\n - _pulseType_: -1 for DN pulse, +1 for UP pulse.\n - _pulseTime_: Time at which the pulse occurred.\n - _channel_: Channel in which the pulse occurred.\n - _band_: 0 for EEG band, 1 for HFO band\n#### task-SNN.csv\nThe SNN file is formatted as follows:\n - _time_: Time at which the SNN neuron activated.\n - _neuronId_: Number id of the SNN neuron (DYNAP-SE numbering from 0 to 1024).\n - _neuronCounter_: Number id of the SNN neuron (sequential numbering from 0 to 40).\n - _moduleName_: Population (ACC_4_0; ACC_0_4), band (EEG; HFO) and module number (ch 0-7) of the neuron.\n   - ACC_4_0 = ACC UP\n   - ACC_0_4 = ACC DN\n - _moduleId_: Module number (0-7).\n - _channelId_: Channel for which the SNN neuron activated.\n#### task-EV.csv\nThe EV file contains annotations of the detected epileptiform patterns with the following format:\n - _time_: time of the detected event.\n - _channelId_: channel id of the detected event.\n - _location_:  channel name of the detected event.\n## Contact Information\nFor inquiries or additional information, please contact Filippo.Costa@usz.ch or Johannes.Sarnthein@usz.ch\n## Acknowledgements\nWe thank V. Dimakopoulos for help in reformatting the data to BIDS. We acknowledge a grant awarded by the Swiss National Science Foundation (funded by the SNSF 204651 to JS and GI with GR and NK as project partners). The funder had no role in the design or analysis of the study.","recording_modality":["ieeg"],"senior_author":"Johannes Sarnthein","sessions":["SITUATION1A","SITUATION2A"],"size_bytes":473061643,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["acute"],"timestamps":{"digested_at":"2026-04-22T12:27:07.985862+00:00","dataset_created_at":"2024-01-30T10:41:43.220Z","dataset_modified_at":"2024-04-11T09:00:21.000Z"},"total_files":44,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004944","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"3e3a4a0b3e51e6c5","model":"openai/gpt-5.2","tagged_at":"2026-01-20T17:37:07.323058+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Other"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.9,"modality":0.6,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot example by population/goal is the pediatric epilepsy HFO dataset (Pathology=Epilepsy, Modality=Resting State, Type=Clinical/Intervention). It shows the convention that when participants are explicitly epilepsy patients and the dataset purpose is biomarker/detection work, Type should be Clinical/Intervention. A second relevant convention is the intraoperative stimulation SEP dataset (Pathology=Surgery), which indicates intraoperative recordings are typically treated as clinical/procedural rather than a standard cognitive task; this supports choosing Clinical/Intervention for Type here as well.","metadata_analysis":"Key extracted facts from the README:\n1) Clinical recruited population: \"intraoperative Electrocorticography (ECoG) from 22 patients undergoing resective epilepsy surgery.\"\n2) Clinical intervention context / pre-post: \"pre-resection recording (referred to as SITUATION1A) and post-resection recording (referred to as SITUATION2A).\"\n3) Primary purpose is epileptiform detection / biomarker pipeline: \"used to detect epileptiform patterns in the ECoG\" and \"annotations of the detected epileptiform patterns\" (both offline and online neuromorphic approaches are mentioned).\n4) No explicit sensory stimulation/task paradigm is described; the dataset description focuses on recording/processing files and detection outputs rather than stimuli.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"22 patients undergoing resective epilepsy surgery\" (explicit epilepsy surgical patients).\n- Few-shot suggests: Epilepsy when epilepsy patients are recruited (matches pediatric epilepsy example).\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: intraoperative ECoG recordings, organized pre/post resection; no explicit stimuli/task described (e.g., no auditory/visual/tactile cues).\n- Few-shot suggests: Similar epilepsy biomarker datasets without explicit stimuli are sometimes labeled Resting State (pediatric epilepsy HFO during sleep) or Other (intraoperative procedural datasets).\n- Alignment: PARTIAL/UNCLEAR because this is intraoperative ECoG (not explicitly resting-state eyes-open/closed, and not explicitly anesthesia).\n\nType:\n- Metadata says: \"detect epileptiform patterns in the ECoG\" and focuses on a neuromorphic processing pipeline and annotations of pathological events.\n- Few-shot suggests: When the primary aim is clinical biomarker/detection in a clinical cohort, label Type as Clinical/Intervention (as in the epilepsy HFO example).\n- Alignment: ALIGN.","decision_summary":"Top-2 candidates and final choices:\n\nPathology:\n- Candidate 1: Epilepsy — Supported by: \"22 patients undergoing resective epilepsy surgery\"; \"detect epileptiform patterns\"; multiple mentions of \"epileptiform patterns\".\n- Candidate 2: Surgery — Intraoperative setting could suggest Surgery, but recruitment is explicitly for epilepsy surgery patients (epilepsy is the defining condition).\n- Final: Epilepsy (metadata explicitly names epilepsy; aligns with few-shot epilepsy convention). Confidence=0.9 based on 3+ explicit epilepsy-related quotes/phrases and strong few-shot alignment.\n\nModality:\n- Candidate 1: Other — Supported by lack of described sensory stimuli/task and the intraoperative ECoG/clinical recording context (procedural recording rather than stimulus-driven sensory modality).\n- Candidate 2: Resting State — Could be argued because recordings appear to be spontaneous/background ECoG pre/post resection, but the metadata does not describe a canonical resting paradigm (e.g., eyes open/closed) and it is intraoperative.\n- Final: Other. Confidence=0.6 (inference from absence of stimulus description; not enough explicit evidence to call Resting State or Anesthesia).\n\nType:\n- Candidate 1: Clinical/Intervention — Supported by: \"intraoperative\"; \"resective epilepsy surgery\"; pre/post resection structure; explicit goal to \"detect epileptiform patterns\" with provided clinical-style annotations.\n- Candidate 2: Other — Could be considered because it is also a methods/neuromorphic pipeline dataset, but the central use is still clinical epileptiform detection in surgical patients.\n- Final: Clinical/Intervention. Confidence=0.8 (2+ strong clinical-purpose quotes plus few-shot alignment with epilepsy biomarker datasets)."}},"nemar_citation_count":1,"computed_title":"Dataset of BCI2000-compatible intraoperative ECoG with neuromorphic encoding","nchans_counts":[{"val":3,"count":10},{"val":6,"count":6},{"val":5,"count":6},{"val":4,"count":5},{"val":23,"count":3},{"val":20,"count":2},{"val":19,"count":2},{"val":22,"count":1},{"val":15,"count":1},{"val":27,"count":1},{"val":2,"count":1},{"val":28,"count":1},{"val":17,"count":1},{"val":11,"count":1},{"val":10,"count":1},{"val":25,"count":1},{"val":21,"count":1}],"sfreq_counts":[{"val":2000.0,"count":44}],"stats_computed_at":"2026-04-22T23:16:00.308724+00:00","total_duration_s":10986.6665,"author_year":"Costa2024","canonical_name":null,"name_source":"canonical"}}