{"success":true,"database":"eegdash","data":{"_id":"69d16e05897a7725c66f4cce","dataset_id":"nm000271","associated_paper_doi":null,"authors":["Wenwen Chang","Weixuan Kong","Guanghui Yan","Renjie Lv","Kaiyue Du","Muhammad Tariq Sadiq","Bin Guo","Rong Yin","Xuan Liu"],"bids_version":"1.9.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.1038/s41597-025-06147-6","datatypes":["eeg"],"demographics":{"subjects_count":28,"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/nm000271","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"19d55bd30570963ddb0ad2d91cce6d884518fd60ba272d4f86c1928ce439b257","license":"CC-BY-4.0","n_contributing_labs":null,"name":"Chang et al. 2025 — A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises (Lanzhou Jiaotong University)","readme":null,"recording_modality":["eeg"],"senior_author":null,"sessions":["0","1","2","3","4","5"],"size_bytes":3715830644,"source":"nemar","storage":{"backend":"s3","base":"s3://openneuro.org/nm000271","raw_key":"dataset_description.json","dep_keys":[]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-04-22T12:52:19.920010+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":113,"computed_title":"Chang et al. 2025 — A multi-paradigm EEG dataset for studying upper limb rehabilitation exercises (Lanzhou Jiaotong University)","nchans_counts":[{"val":59,"count":113}],"sfreq_counts":[{"val":1000.0,"count":113}],"stats_computed_at":"2026-04-22T23:16:00.314415+00:00","total_duration_s":20969.887,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"d6ea3f9d00c2174b","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Unknown"],"modality":["Visual"],"type":["Motor"],"confidence":{"pathology":0.4,"modality":0.6,"type":0.7},"reasoning":{"few_shot_analysis":"Most similar few-shot example by paradigm is the “EEG Motor Movement/Imagery Dataset”, which includes explicit motor imagery tasks and is labeled Type=Motor and Modality=Visual (because the imagery/movement is typically cued by on-screen targets). This guides the convention that an “imagery” task is commonly treated as a motor imagery paradigm (Type=Motor), with Visual modality when cues are screen-based. However, unlike the few-shot, the current metadata does not explicitly state the imagery domain (motor vs visual) or the cue modality, so the few-shot can only guide inference, not establish facts.","metadata_analysis":"Available metadata is extremely sparse. Key snippets: (1) title: \"chang2025 - NEMAR Dataset\"; (2) participants: \"Subjects: 28\"; (3) tasks list contains \"imagery\" (along with \".gitignore\" and \"Chang2025.metadata\"). There is no explicit description of stimulus type, clinical population, or experimental instructions beyond the task name \"imagery\".","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology: Metadata says only \"Subjects: 28\" (no diagnosis/condition). Few-shot patterns cannot determine pathology without explicit recruitment info. ALIGNMENT: not applicable; metadata is insufficient, so label remains Unknown.\n\nModality: Metadata says task includes \"imagery\" but does not specify stimulus channel (visual/auditory/tactile) or whether there were cues at all. Few-shot convention for motor imagery datasets often uses Modality=Visual due to screen cues (as in the motor movement/imagery example). ALIGNMENT: weak/uncertain because metadata lacks cue description; few-shot suggests Visual but cannot be confirmed.\n\nType: Metadata explicitly includes task name \"imagery\". Few-shot convention maps imagery paradigms to Type=Motor when they are motor imagery tasks. ALIGNMENT: partial—metadata supports “imagery” but does not specify it is motor imagery; still, Motor is the closest allowed Type given conventions.","decision_summary":"Pathology top-2: (1) Unknown—supported by lack of any clinical descriptors (\"Subjects: 28\" only). (2) Healthy—possible default assumption for many NEMAR datasets, but not stated. Decision: Unknown (metadata insufficient). Confidence 0.4 because no explicit evidence.\n\nModality top-2: (1) Visual—supported only by few-shot convention that imagery tasks are often visually cued (motor movement/imagery example labeled Visual). (2) Motor—possible if interpreting “imagery” as primarily motor-domain without considering cue modality, but modality is defined as stimulus channel and motor imagery typically uses visual cues. Decision: Visual, but weakly supported. Confidence 0.6 (contextual inference + few-shot analog, no direct metadata quote about stimuli).\n\nType top-2: (1) Motor—best match to an imagery task under EEGDash conventions (motor imagery). (2) Other—if imagery were non-motor (e.g., visual imagery) or unspecified. Decision: Motor. Confidence 0.7 (one explicit metadata cue: task name \"imagery\" + reasonable convention-based inference)."}},"canonical_name":null,"name_confidence":0.66,"name_meta":{"suggested_at":"2026-04-14T10:18:35.344Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Chang2025_2"}}