{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3382","dataset_id":"ds004774","associated_paper_doi":null,"authors":["M.A. van den Boom","N.M. Gregg","G.O. Valencia","B.N. Lundstrom","K.J. Miller","D. van Blooijs","G.J.M. Huiskamp","F.S.S. Leijten","G.A. Worrell","D. Hermes"],"bids_version":"Brain Imaging Data Structure Specification v1.8.0","contact_info":["Max van den Boom"],"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds004774.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":9,"ages":[16,41,62,65,36,11,12,9,14,16,7,50,8,6],"age_min":6,"age_max":65,"age_mean":25.214285714285715,"species":null,"sex_distribution":{"f":7,"m":7},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004774","osf_url":null,"github_url":null,"paper_url":null},"funding":["NIMH R01MH122258","Epilepsy Foundation of the Netherlands/Dutch Epilepsy Foundation, NEF17-07"],"ingestion_fingerprint":"6972a82828e660bb313edf2e4713405cad928d7429a8eda1edd81d1bcb07511e","license":"CC0","n_contributing_labs":null,"name":"Automatic Evoked Response Detection (ER-Detect) dataset","readme":null,"recording_modality":["ieeg"],"senior_author":"D. Hermes","sessions":["1","1b"],"size_bytes":26608945134,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["SPESclin"],"timestamps":{"digested_at":"2026-04-22T12:26:51.297648+00:00","dataset_created_at":"2023-09-25T14:40:46.435Z","dataset_modified_at":"2023-12-30T23:56:46.000Z"},"total_files":9,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004774","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"a52cfa2c301facb5","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Epilepsy"],"modality":["Other"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.6,"modality":0.6,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot reference is the pediatric epilepsy HFO dataset (Pathology=Epilepsy, Type=Clinical/Intervention), which reflects a labeling convention that datasets built around clinical EEG procedures/biomarker detection in a patient population are categorized as Clinical/Intervention rather than a cognitive construct. While the ER-Detect dataset is not sleep/HFO, it similarly appears to be a clinical neurophysiology dataset centered on detecting evoked responses (CCEP/SPES), so the same convention guides selecting Type=Clinical/Intervention and a clinical-pathology label rather than Healthy.\nA secondary stylistic reference is the Parkinson’s oddball dataset labeled Clinical/Intervention: it shows that when the primary focus is characterizing/quantifying clinically relevant neural markers in a clinical cohort, Type is Clinical/Intervention even if there is a task paradigm.","metadata_analysis":"Key metadata facts:\n1) Title indicates a clinical/technical evoked-response focus: \"Automatic Evoked Response Detection (ER-Detect) dataset\".\n2) Tasks are explicitly: \"SPESclin\" and \"ccep\".\n3) Participants are a mixed-age clinical cohort size typical for invasive/clinical studies: \"Subjects: 14\" with \"Age range: 6-65\".\nInterpretation from these phrases: SPES (single-pulse electrical stimulation) and CCEP (cortico-cortical evoked potentials) are typically recorded in intracranial clinical monitoring contexts (commonly presurgical epilepsy evaluation), suggesting a clinical recruited population and a clinical/methodological aim rather than a sensory-perceptual experiment.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: tasks are \"SPESclin\" and \"ccep\" (clinical stimulation/evoked potentials) and provides only demographics (\"Age range: 6-65\") without naming a diagnosis.\n- Few-shot pattern suggests: clinical neurophysiology datasets in patients are labeled with the recruiting pathology when known (e.g., Epilepsy) or with a clinical label (e.g., Surgery) when perioperative.\n- Alignment: PARTIAL. Metadata implies a clinical cohort but does not explicitly state epilepsy vs other surgical indications.\n- Resolution: choose Epilepsy as the most likely recruiting condition for SPES/CCEP datasets, but keep confidence moderate because the diagnosis is not explicitly stated.\n\nModality:\n- Metadata says: tasks are \"SPESclin\" and \"ccep\".\n- Few-shot pattern suggests: modality is the stimulus/input channel; when not classic sensory (auditory/visual/tactile/motor) and involves clinical stimulation, use \"Other\".\n- Alignment: ALIGN. Electrical stimulation is not one of the standard sensory modalities in the allowed list.\n\nType:\n- Metadata says: \"Automatic Evoked Response Detection\" and tasks \"SPESclin\"/\"ccep\".\n- Few-shot pattern suggests: when the dataset focus is clinical procedures/biomarkers/methods in patient recordings, label Type as \"Clinical/Intervention\".\n- Alignment: ALIGN. The dataset appears centered on detecting evoked responses from clinical stimulation, consistent with Clinical/Intervention rather than Perception/Attention/etc.","decision_summary":"Top-2 candidates and selection:\n\nPathology:\n1) Epilepsy (selected): supported indirectly by \"SPESclin\" and \"ccep\", which are most commonly collected during presurgical epilepsy monitoring; small N=14 and broad age range (\"6-65\") also fits clinical invasive cohorts.\n2) Surgery (runner-up): also plausible because SPES/CCEP are used in surgical mapping contexts; however metadata does not explicitly mention surgery/perioperative setting.\nDecision: Epilepsy wins as the most typical recruiting diagnosis for CCEP/SPES datasets, but evidence is inferential (no explicit diagnosis stated) → moderate confidence.\n\nModality:\n1) Other (selected): tasks \"SPESclin\"/\"ccep\" imply direct electrical stimulation rather than auditory/visual/tactile.\n2) Multisensory (runner-up): unlikely because no sensory stimuli are described.\nDecision: Other clearly best.\n\nType:\n1) Clinical/Intervention (selected): title \"Automatic Evoked Response Detection\" + clinical stimulation tasks (\"SPESclin\", \"ccep\") indicate a clinical/methodological evoked-response purpose.\n2) Other (runner-up): could be framed as general connectivity/methods without explicit clinical outcomes, but the task naming strongly suggests clinical neurophysiology.\nDecision: Clinical/Intervention best supported.\n\nConfidence justification (quotes/features):\n- Pathology confidence is limited because there is no explicit diagnostic phrase; inference relies on task labels \"SPESclin\"/\"ccep\".\n- Modality and Type are more directly supported by the same explicit task labels and the evoked-response detection focus in the title."}},"computed_title":"Automatic Evoked Response Detection (ER-Detect) dataset","nchans_counts":[{"val":133,"count":6},{"val":68,"count":2},{"val":39,"count":2},{"val":97,"count":1},{"val":89,"count":1},{"val":65,"count":1},{"val":130,"count":1}],"sfreq_counts":[{"val":2048.0,"count":14}],"stats_computed_at":"2026-04-22T23:16:00.308283+00:00","total_duration_s":43012.01513671875,"canonical_name":null,"name_confidence":0.74,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Boom2023"}}