{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a339a","dataset_id":"ds004859","associated_paper_doi":null,"authors":["Kazuki Sakakura","Eishi Asano"],"bids_version":"1.7.0","contact_info":["Kaz Sakakura"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004859.v1.0.0","datatypes":["ieeg"],"demographics":{"subjects_count":7,"ages":[14,17,17,17,17,10,14],"age_min":10,"age_max":17,"age_mean":15.142857142857142,"species":null,"sex_distribution":{"m":3,"f":4},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004859","osf_url":null,"github_url":null,"paper_url":null},"funding":["N/A"],"ingestion_fingerprint":"d6f0b72e69902b7859e411907f4fa6df2de2febbd050cdac45150eb2b00c67a3","license":"CC0","n_contributing_labs":null,"name":"iEEG on children during Stroop task","readme":null,"recording_modality":["ieeg"],"senior_author":"Eishi Asano","sessions":["01","02"],"size_bytes":2435511071,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["stroop"],"timestamps":{"digested_at":"2026-04-22T12:27:03.577238+00:00","dataset_created_at":"2023-11-22T04:07:20.769Z","dataset_modified_at":"2023-11-22T04:43:12.000Z"},"total_files":9,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004859","raw_key":"dataset_description.json","dep_keys":["CHANGES","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"7be9ab7280e35f97","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Visual"],"type":["Attention"],"confidence":{"pathology":0.55,"modality":0.7,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot by paradigm/construct is the DPX cognitive control dataset (\"EEG: DPX Cog Ctl Task in Acute Mild TBI\"), which is labeled Type=Attention and Modality=Visual. Although DPX is not Stroop, both are classic cognitive control/attention-demanding paradigms with visually presented cues. This guides mapping Stroop → Attention (Type) and likely Visual (Modality). Few-shot examples also show that when a dataset is explicitly clinical (e.g., \"Parkinsons disease\", \"mTBI\", \"schizophrenia\"), Pathology is set to that diagnosis; however, this dataset’s provided metadata does not explicitly name a diagnosis, so we should not force a clinical label based only on iEEG convention.","metadata_analysis":"Key provided metadata facts:\n1) Title indicates intracranial EEG and the task: \"iEEG on children during Stroop task\".\n2) Pediatric sample is explicit: \"Age range: 10-17\" and \"Subjects: 7\".\n3) Task label is explicit: tasks include \"stroop\".\nNo metadata snippet explicitly states a recruitment diagnosis (e.g., epilepsy) or surgical context, beyond the iEEG mention in the title.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: only \"iEEG on children\" and \"Age range: 10-17\"; no diagnosis is stated.\n- Few-shot pattern suggests: iEEG datasets are often from surgical epilepsy patients, but few-shot examples only assign Epilepsy/Surgery when explicitly stated.\n- Alignment: CONFLICT/INSUFFICIENT—metadata lacks an explicit clinical recruitment label, so we cannot assign Epilepsy/Surgery as fact. Choose Unknown.\n\nModality:\n- Metadata says: \"Stroop task\" / task name \"stroop\".\n- Few-shot pattern suggests: cognitive control tasks like DPX are typically visually cued and labeled Visual.\n- Alignment: ALIGNS—Stroop is conventionally visually presented (color words), consistent with Visual.\n\nType:\n- Metadata says: task is \"Stroop\" (\"iEEG on children during Stroop task\"; tasks: \"stroop\").\n- Few-shot pattern suggests: cognitive control/attention-demanding paradigms (e.g., DPX) map to Type=Attention.\n- Alignment: ALIGNS—Stroop primarily probes selective attention / interference control, matching Attention.","decision_summary":"Top-2 candidates (with head-to-head):\n\nPathology:\n1) Unknown — Supported by absence of any explicit diagnosis in provided fields; only age/task are given (\"Age range: 10-17\", \"Stroop task\").\n2) Surgery (or Epilepsy) — Weak inference from \"iEEG\" implying invasive clinical monitoring, but not explicitly stated.\nDecision: Unknown (metadata lacks explicit recruitment diagnosis; inference-only runner-up).\nConfidence basis: only indirect iEEG convention vs no explicit diagnostic quotes.\n\nModality:\n1) Visual — Inferred from \"Stroop\" (typically visually presented color-word stimuli) and consistent with few-shot DPX (Visual + Attention for cognitive control tasks).\n2) Unknown — If Stroop variant were auditory (not stated), modality could differ.\nDecision: Visual.\nConfidence basis: one explicit task quote (\"Stroop\") + strong convention/few-shot analog.\n\nType:\n1) Attention — Stroop is a canonical selective attention/interference control task; aligns with few-shot DPX cognitive control labeled Attention.\n2) Decision-making — Possible alternative if focus were response conflict choices, but Stroop is more standardly categorized as attention/control.\nDecision: Attention.\nConfidence basis: explicit task quote (\"stroop\") + strong few-shot mapping of similar cognitive control paradigms to Attention."}},"nemar_citation_count":0,"computed_title":"iEEG on children during Stroop task","nchans_counts":[{"val":128,"count":8},{"val":108,"count":1}],"sfreq_counts":[{"val":1000.0,"count":9}],"stats_computed_at":"2026-04-22T23:16:00.308614+00:00","total_duration_s":null,"canonical_name":null,"name_confidence":0.78,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Sakakura2023_children_Stroop"}}