{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3372","dataset_id":"ds004625","associated_paper_doi":null,"authors":["Chang Liu","Ryan J. Downey","Jacob S. Salminen","Sofia Arvelo Rojas","Erika M. Pliner","Natalie Richer","Jungyun Hwang","Yenisel Cruz-Almeida","Todd M. Manini","Chris J. Hass","Rachael D. Seidler","David J. Clark","Daniel P. Ferris"],"bids_version":"v1.0.0","contact_info":["Chang Liu","Daniel Ferris","Jacob Salminen"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004625.v1.0.2","datatypes":["eeg"],"demographics":{"subjects_count":32,"ages":[32,29,21,20,20,26,23,23,24,20,25,21,21,22,20,20,21,22,26,20,24,22,24,26,26,30,28,25,35,28,27,20],"age_min":20,"age_max":35,"age_mean":24.09375,"species":null,"sex_distribution":{"m":16,"f":16},"handedness_distribution":{"r":29,"l":3}},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds004625","osf_url":null,"github_url":null,"paper_url":null},"funding":["This study was supported by the National Institute of Health (U01AG061389)."],"ingestion_fingerprint":"eb00e9afcd34b86e1bb89183f32ceefb64103b3a303e1c022aef8ca0abfd7753","license":"CC0","n_contributing_labs":null,"name":"Mind in Motion Young Adults Walking Over Uneven Terrain","readme":"Our dataset contains high-density, dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration, ground reaction forces, head model constructed from T1 structural MR images from 32 participants walking over uneven terrain and at different speeds. Participants completed two trials for each condition for three minutes and a seated rest trial for three minutes. Digitized electrode locations (txt) are included in each subject folder.\nPlease refer to our publication for more detail.\nThis study was supported by the National Institute of Health (U01AG061389).","recording_modality":["eeg"],"senior_author":"Daniel P. Ferris","sessions":[],"size_bytes":67068941232,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["0p25","0p5","0p75","1p0","flat","high","low","med","rest"],"timestamps":{"digested_at":"2026-04-22T12:26:46.077628+00:00","dataset_created_at":"2023-07-02T01:56:00.027Z","dataset_modified_at":"2023-07-04T18:08:12.000Z"},"total_files":543,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004625","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv","task-UnevenTerrain_events.json"]},"nemar_citation_count":0,"computed_title":"Mind in Motion Young Adults Walking Over Uneven Terrain","nchans_counts":[{"val":284,"count":323},{"val":310,"count":187},{"val":375,"count":33}],"sfreq_counts":[{"val":500.0,"count":543}],"stats_computed_at":"2026-04-22T23:16:00.308093+00:00","tags":{"pathology":["Healthy"],"modality":["Motor"],"type":["Motor"],"confidence":{"pathology":0.7,"modality":0.85,"type":0.85},"reasoning":{"few_shot_analysis":"Most similar few-shot by paradigm is the “EEG Motor Movement/Imagery Dataset” (Schalk et al.), which is labeled Type=Motor because the primary purpose is studying movement/imagery during motor behavior. Although that example uses visual targets (hence Modality=Visual there), the labeling convention it illustrates is that when the core experimental manipulation is movement execution/imagery, Type should be Motor. The current dataset’s core manipulation is walking over uneven terrain at different speeds, which maps to Motor in the same way.","metadata_analysis":"Key metadata indicates gait/movement as the central paradigm: (1) “32 participants walking over uneven terrain and at different speeds.” (2) “Participants completed two trials for each condition for three minutes and a seated rest trial for three minutes.” Tasks listed also emphasize locomotion conditions: “flat, high, low, med” and multiple speed conditions “0p25, 0p5, 0p75, 1p0”, plus “rest”. No recruitment based on a clinical diagnosis is mentioned; participants are described as “Young Adults” with “Age range: 20-35”.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says participants are “Young Adults” with “Age range: 20-35” and gives no diagnosis/clinical recruitment information; few-shot convention suggests labeling such non-clinical cohorts as Healthy. ALIGN (metadata lacks pathology; convention fills as Healthy rather than a disorder).\nModality: Metadata says “walking over uneven terrain and at different speeds” (movement execution); few-shot convention for movement-focused paradigms supports Motor as the dominant modality when the task is bodily movement rather than external sensory stimulation. ALIGN.\nType: Metadata says the study records EEG during “walking over uneven terrain and at different speeds” with multiple locomotion conditions; few-shot convention maps movement-execution paradigms to Type=Motor. ALIGN.","decision_summary":"Pathology top-2: (1) Healthy—supported by non-clinical description “Young Adults” and no mention of patient recruitment/diagnosis, plus typical convention in few-shot for normative cohorts; (2) Unknown—because metadata never explicitly states “healthy” or “controls”. Final: Healthy (inference from absence of clinical recruitment). Confidence reflects lack of explicit ‘healthy’ statement.\nModality top-2: (1) Motor—supported by “walking over uneven terrain and at different speeds” and locomotion-condition task list; (2) Resting State—because there is “a seated rest trial for three minutes” and a “rest” task entry. Final: Motor because most tasks/conditions are walking manipulations and rest is a single condition. Confidence based on multiple explicit gait/walking mentions.\nType top-2: (1) Motor—primary construct is locomotion over terrain/speed conditions; (2) Resting-state—rest trial exists but is not the primary paradigm. Final: Motor."}},"total_duration_s":102893.018,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"9f7768cf817dbd0c","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"author_year":"Liu2023","canonical_name":null}}