{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a339c","dataset_id":"ds004865","associated_paper_doi":null,"authors":["Haydn G. Herrema","Michael J. Kahana"],"bids_version":"1.7.0","contact_info":["Haydn Herrema"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004865.v2.0.1","datatypes":["ieeg"],"demographics":{"subjects_count":42,"ages":[34,20,41,34,44,23,48,33,45,15,48,20,20,52,31,50,18,28,38,26,56,26,55,37,57,40,28,43,28,48,30,19,24,27,55,18,27,20,37,42,30,40,25,41,39,25,24,29],"age_min":15,"age_max":57,"age_mean":34.125,"species":null,"sex_distribution":{"f":21,"m":27},"handedness_distribution":{"r":35,"l":9,"a":2}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004865","osf_url":null,"github_url":null,"paper_url":null},"funding":["NIH: MH055687","NIH: MH061975"],"ingestion_fingerprint":"93d15ea491c4e54aeb37447bf1a0ecdc700d94ea9585011f53716d34b7004d5d","license":"CC0","n_contributing_labs":null,"name":"pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study","readme":"### pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study\n#### Description\nThis dataset contains behavioral events and intracranial electrophysiological recordings from a delayed free recall task.  The experiment consists of participants studying a list of words, presented visually one at a time, completing simple arithmetic problems that function as a distractor, and then freely recalled the words from the just-presented list in any order.  The data was collected at clinical sites across the country as part of a collaboration with the Computational Memory Lab at the University of Pennsylvania.\nThis study was a preliminary cogntive electrophysiology study undertaken by the Computational Memory Lab, and is a predecessor to the following datasets: [FR1](https://openneuro.org/datasets/ds004789) & [CatFR1](https://openneuro.org/datasets/ds004809)\n#### To Note\n* The iEEG recordings are labeled either \"monopolar\" or \"bipolar.\"  The monopolar recordings are referenced (typically a mastoid reference), but should always be re-referenced before analysis.  The bipolar recordings are referenced according to a paired scheme indicated by the accompanying bipolar channels tables.\n* Each subject has a unique montage of electrode locations.  MNI and Talairach coordinates are provided when available, along with brain region annotations.\n* Recordings were made on multiple different systems, so we have done the scaling to provide all voltage values in V.\n#### Contact\nFor questions or inquiries, please contact sas-kahana-sysadmin@sas.upenn.edu.","recording_modality":["ieeg"],"senior_author":"Michael J. Kahana","sessions":["0","1","2","3","5"],"size_bytes":104999471870,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["pyFR"],"timestamps":{"digested_at":"2026-04-22T12:27:03.787797+00:00","dataset_created_at":"2023-11-29T19:38:59.142Z","dataset_modified_at":"2024-04-22T22:04:43.000Z"},"total_files":172,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004865","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"4a051be509a0e3d0","metadata_hash":"bf8ef5f5f2677753","model":"openai/gpt-5.2","tagged_at":"2026-01-20T17:35:43.717428+00:00"},"tags":{"pathology":["Surgery"],"modality":["Visual"],"type":["Memory"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Closest convention match for Type is the few-shot example 'EEG, pupillometry... digit span task and rest', which is labeled Type='Memory' because it is a word/digit list encoding + recall working-memory paradigm. This dataset is also an explicit list-learning and free-recall paradigm, so the same Type convention (Memory) applies. For Modality, the schizophrenia visual discrimination example shows that when stimuli are visually presented on a screen, Modality='Visual' (stimulus channel drives Modality labeling). For Pathology, few-shot examples labeled 'Surgery' and 'Epilepsy' demonstrate that explicit clinical recruitment statements are used when present; here, no diagnosis is explicitly stated, so we must rely on weaker contextual inference.","metadata_analysis":"Key task/stimulus facts: (1) \"participants studying a list of words, presented visually one at a time\" (supports Visual modality). (2) \"intracranial electrophysiological recordings\" and \"data was collected at clinical sites across the country\" (indicates a clinical/surgically-implanted recording context). Key construct facts: (3) \"delayed free recall task\" and \"then freely recalled the words from the just-presented list in any order\" (supports Memory type).","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says \"intracranial electrophysiological recordings\" at \"clinical sites\" but does NOT name a diagnosis (no quote mentioning epilepsy, Parkinson's, etc.). Few-shot patterns suggest intracranial recordings commonly come from surgical/clinical implantation contexts (sometimes epilepsy), but this is an inference. ALIGNMENT: partial (clinical context aligns), but diagnosis unspecified, so we choose the most defensible recruited-context label ('Surgery') with moderate confidence.\nModality: Metadata says \"words, presented visually\"; few-shot conventions label visually presented paradigms as Modality='Visual'. ALIGNMENT: strong alignment.\nType: Metadata explicitly describes a delayed free recall memory paradigm (study words + distractor + free recall). Few-shot digit-span example maps list encoding/recall paradigms to Type='Memory'. ALIGNMENT: strong alignment.","decision_summary":"Pathology top-2: (A) Surgery — supported by \"intracranial electrophysiological recordings\" and collection at \"clinical sites\" (implanted electrodes imply a surgical patient context); (B) Epilepsy/Unknown — epilepsy is plausible for iEEG but not explicitly stated, and Unknown is possible due to missing diagnosis. Winner: Surgery (more specific than Unknown, less assumption-heavy than Epilepsy). Confidence limited because no explicit diagnosis is named.\nModality top-2: (A) Visual — supported by \"words, presented visually\"; (B) Other — only if presentation channel were unspecified (not the case). Winner: Visual.\nType top-2: (A) Memory — supported by \"delayed free recall task\" and \"freely recalled the words\"; (B) Attention — distractor arithmetic exists, but the primary aim is episodic recall. Winner: Memory."}},"nemar_citation_count":0,"computed_title":"pyFR: Delayed Free Recall of Word Lists, Preliminary Cognitive Electrophysiology Study","nchans_counts":[{"val":100,"count":7},{"val":80,"count":5},{"val":74,"count":5},{"val":131,"count":5},{"val":62,"count":4},{"val":53,"count":4},{"val":108,"count":4},{"val":85,"count":4},{"val":54,"count":4},{"val":110,"count":4},{"val":46,"count":4},{"val":86,"count":4},{"val":104,"count":3},{"val":168,"count":3},{"val":150,"count":3},{"val":32,"count":3},{"val":84,"count":3},{"val":72,"count":3},{"val":70,"count":3},{"val":27,"count":3},{"val":105,"count":3},{"val":55,"count":3},{"val":121,"count":3},{"val":42,"count":3},{"val":78,"count":3},{"val":82,"count":3},{"val":109,"count":3},{"val":48,"count":3},{"val":88,"count":3},{"val":116,"count":3},{"val":123,"count":3},{"val":96,"count":3},{"val":47,"count":3},{"val":91,"count":3},{"val":75,"count":3},{"val":149,"count":2},{"val":142,"count":2},{"val":111,"count":2},{"val":63,"count":2},{"val":68,"count":2},{"val":36,"count":2},{"val":76,"count":2},{"val":119,"count":2},{"val":102,"count":2},{"val":144,"count":2},{"val":124,"count":2},{"val":126,"count":2},{"val":52,"count":2},{"val":57,"count":2},{"val":130,"count":2},{"val":87,"count":2},{"val":153,"count":2},{"val":58,"count":2},{"val":97,"count":1},{"val":94,"count":1},{"val":64,"count":1},{"val":98,"count":1},{"val":81,"count":1},{"val":203,"count":1},{"val":160,"count":1},{"val":95,"count":1},{"val":187,"count":1},{"val":56,"count":1},{"val":90,"count":1},{"val":101,"count":1},{"val":120,"count":1}],"sfreq_counts":[{"val":1000.0,"count":102},{"val":512.0,"count":40},{"val":2000.0,"count":16},{"val":400.0,"count":8},{"val":499.7071,"count":6}],"stats_computed_at":"2026-04-22T23:16:00.308637+00:00","total_duration_s":650265.1039779703,"canonical_name":null,"name_confidence":0.95,"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":"Herrema2023_pyFR_Delayed_Free"}}