{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3456","dataset_id":"ds006525","associated_paper_doi":null,"authors":["Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma"],"bids_version":"1.8.0","contact_info":["Lei Ding"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds006525.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":34,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds006525","osf_url":null,"github_url":null,"paper_url":null},"funding":["OPTIONAL. List of sources of funding (grant numbers)"],"ingestion_fingerprint":"bfdeeb70a4bb4170fee783318981fa77cc3ee1dbc5162c750d90ddc4c2d11d17","license":"CC0","n_contributing_labs":null,"name":"Resting EEG","readme":"Introduction: The EEG data was recorded using the 128-channel Amps 300 amplifier (Electrical Geodesics Inc., OR, USA) at a sampling frequency of 1000 Hz.\nThe EEG data acquisition was conducted  during the resting.\nStructural MRI data for the same participants were acquired at the University of Oklahoma Health Science Center (OUHSC) MRI facility using a GE MR750 scanner. The scans were obtained with GE’s BRAVO sequence, with a field of view (FOV) of 240 mm and 180 axial slices per slab\nPreprocessing in EEGLAB: After the data acquisition, a band-pass filter (0.5–100 Hz) and a notch filter (58–62 Hz) were applied to remove noise.\nNoisy channels and artifacts (e.g., from eye blinks, muscle movements, or heartbeats) were identified and removed.\nBad channels were replaced using interpolation, and the data was re-referenced to the average of all electrodes.\nThe data was then sampled down to 250 Hz to reduce file size while keeping enough detail. No data segments were removed to ensure the continuity needed for later analysis.","recording_modality":["eeg"],"senior_author":"Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma","sessions":[],"size_bytes":3223158965,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["resting"],"timestamps":{"digested_at":"2026-04-22T12:29:31.761612+00:00","dataset_created_at":"2025-08-01T16:44:42.411Z","dataset_modified_at":"2025-08-01T17:19:32.000Z"},"total_files":34,"storage":{"backend":"s3","base":"s3://openneuro.org/ds006525","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"7202d9f5bff14cd4","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Resting State"],"type":["Resting-state"],"confidence":{"pathology":0.4,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"Closest few-shot conventions are the explicitly resting-state datasets: (1) \"A Resting-state EEG Dataset for Sleep Deprivation\" labeled Modality=Resting State and Type=Resting-state, showing that eyes-open/closed resting paradigms map to Resting State/Resting-state; (2) \"A dataset of EEG recordings from: Alzheimer's disease, Frontotemporal dementia and Healthy subjects\" labeled Resting State / Clinical/Intervention because the clinical cohort is the primary focus. Our target dataset is resting EEG but does not mention any clinical recruitment, so it follows the resting-state convention rather than clinical/intervention.","metadata_analysis":"Key stated facts: (1) The acquisition context is resting: \"The EEG data acquisition was conducted  during the resting.\" (2) The task label confirms it: tasks includes \"resting\". (3) No clinical population is described: participants only states \"Subjects: 34\" with no diagnosis, patient/control grouping, or inclusion/exclusion criteria.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata SAYS only \"Subjects: 34\" (no disorder mentioned). Few-shot pattern SUGGESTS that when no disorder is stated, label as Healthy (e.g., sleep deprivation resting-state example), but this is not an explicit fact here. They partially CONFLICT (inference vs missing info). Winner: metadata-driven conservatism → Unknown.\nModality: Metadata SAYS \"conducted during the resting\" and task \"resting\". Few-shot pattern SUGGESTS mapping resting paradigms to Modality=Resting State. ALIGN.\nType: Metadata SAYS resting acquisition/task (same quotes as above). Few-shot pattern SUGGESTS Type=Resting-state for passive resting EEG datasets. ALIGN.","decision_summary":"Top-2 candidates per category:\n- Pathology: (A) Unknown — supported by lack of any diagnosis/group info: \"Subjects: 34\" only. (B) Healthy — plausible by convention for non-clinical resting datasets, but not explicitly stated. Selected Unknown (insufficient explicit evidence for Healthy). Confidence 0.4 due to missing recruitment/diagnosis details.\n- Modality: (A) Resting State — supported by \"conducted during the resting\" and task \"resting\"; matches few-shot resting-state conventions. (B) Unknown — would apply only if task context were unclear; here it is explicit. Selected Resting State. Confidence 0.8 (2 explicit quotes + strong few-shot analog).\n- Type: (A) Resting-state — supported by \"conducted during the resting\" and task \"resting\"; matches few-shot resting-state conventions. (B) Clinical/Intervention — would require explicit clinical cohort focus (not present). Selected Resting-state. Confidence 0.8 (2 explicit quotes + strong few-shot analog)."}},"computed_title":"Resting EEG","nchans_counts":[{"val":128,"count":26},{"val":129,"count":8}],"sfreq_counts":[{"val":250.0,"count":34}],"stats_computed_at":"2026-04-22T23:16:00.311716+00:00","total_duration_s":null,"author_year":"Neuroimaging2025","canonical_name":null}}