{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3456","dataset_id":"ds006525","associated_paper_doi":"10.1016/j.neuroimage.2022.119461","authors":["Computational Neuroimaging and Neuroengineering Lab ar the University of Oklahoma"],"bids_version":"1.8.0","contact_info":null,"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":{"paper_url":"https://doi.org/10.1016/j.neuroimage.2022.119461"},"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":null,"sessions":[],"size_bytes":3223362408,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["resting"],"timestamps":{"digested_at":"2026-05-31T16:25:08.749675+00:00","dataset_created_at":null,"dataset_modified_at":null},"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":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Resting State"],"type":["Resting-state"],"confidence":{"pathology":0.8,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"The 'Resting EEG' dataset shares similarities with the few-shot example involving dementia where the pathology was labeled 'Dementia' and the type was labeled as 'Resting-state' because it focused on resting-state data collection. However, in this dataset, there is no mention of a specific clinical population like dementia, suggesting a focus on healthy resting-state EEG. Therefore, this guides us to lean towards a 'Healthy' pathology.","metadata_analysis":"The metadata indicates that the EEG data was collected during resting-state: 'The EEG data acquisition was conducted during the resting.' The acquisition details and the term 'resting-state' clearly support that the type of the study is 'Resting-state'.","paper_abstract_analysis":"The abstract highlights the study of functional connectivity dynamics in resting-state EEG and confirms that the research involved resting-state EEG: '... to study spatiotemporal dynamics of FC (dFC) in resting-state human EEG data.' This supports the conclusion that the dataset is categorized under 'Resting-state' type.","evidence_alignment_check":{"Pathology":{"metadata_says":"No explicit clinical population is mentioned, implying a likely focus on a healthy cohort. Terms like 'resting-state human EEG data' are indicative.","few_shot_suggests":"The absence of a specific clinical population and focus solely on resting data suggest 'Healthy' pathology.","alignment":"ALIGN","conclusion":"Healthy due to the lack of clinical pathology focus."},"Modality":{"metadata_says":"Resting-state acquisition is described without specific sensory tasks, suggesting 'Resting State'.","few_shot_suggests":"Resting-state studies generally align with the 'Resting State' modality.","alignment":"ALIGN","conclusion":"Resting State, consistent with the purpose of the study."},"Type":{"metadata_says":"The study focused on dynamics in resting-state EEG: 'study spatiotemporal dynamics of FC (dFC) in resting-state human EEG data'.","few_shot_suggests":"Resting-state categorizations apply to studies focusing on spontaneous brain activity.","alignment":"ALIGN","conclusion":"Resting-state due to the research aim around resting-state EEG dynamics."}},"decision_summary":"The Pathology is labeled 'Healthy' with confidence due to the absence of a specific clinical population, aligning with resting-state studies. Modality is 'Resting State' as all collected data were during resting, without sensory tasks. The Type is 'Resting-state', reflecting the focus on functional connectivity in resting EEG. Each category aligns with available data."}},"computed_title":"Resting EEG","nchans_counts":[{"val":128,"count":26},{"val":129,"count":8}],"sfreq_counts":[{"val":250.0,"count":34}],"stats_computed_at":"2026-05-31T19:34:32.602705+00:00","total_duration_s":null,"author_year":"Neuroimaging2025","canonical_name":null,"bad_channels_info":null,"acknowledgements":"Please cite the paper with doi: 10.1088/1741-2552/ab7ad3 that reported the dataset for the first time.","ethics_approvals":["OPTIONAL. List of ethics committee approvals of the research protocols and/or protocol identifiers"],"generated_by":[{"Name":"BIDScoin","Version":"4.2.1","CodeURL":"https://github.com/Donders-Institute/bidscoin"}],"how_to_acknowledge":"Cite the following paper.\n33.\tShou GF, Yuan H, Li C, Chen YF, Chen YX, and Ding L: Whole-brain Electrophysiological Functional Connectivity Dynamics in Resting-state EEG, Journal of Neural Engineering, 17(2): 026016, 2020.","references_and_links":["OPTIONAL. List of references to publication that contain information on the dataset, or links","https://github.com/Donders-Institute/bidscoin","doi: 10.1088/1741-2552/ab7ad3","doi: 10.1016/j.neuroimage.2022.119461","doi: 10.1016/j.neuroimage.2025.121408"],"associated_paper_meta":{"channel":"openneuro/associatedPaperDOI","confidence":"high","author_overlap":-1,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}