{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c51","dataset_id":"ds007526","associated_paper_doi":null,"authors":["Zoya Katzir","Daniel Vered","Inbal Maidan (inbalm@tlvmc.gov.il)"],"bids_version":"1.10.0","contact_info":["Inbal Maidan"],"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds007526.v1.0.0","datatypes":["eeg"],"demographics":{"subjects_count":144,"ages":[42,60,60,72,47,56,67,62,63,69,61,62,69,54,51,61,65,50,54,62,70,60,55,72,64,66,64,65,70,71,60,65,42,58,73,53,71,67,57,64,70,67,48,65,72,70,74,68,70,70,60,69,53,63,72,62,51,60,52,66,68,59,71,75,53,73,75,83,72,82,76,73,41,69,39,45,55,63,69,70,67,51,59,78,72,70,78,62,64,74,65,68,67,67,71,74,82,73,47,61,68,78,67,54,65,66,48,53,69,68,64,67,65,76,71,71,68,75,67,78,50,65,67,68,75,74,78,57,55,63,64,71,67,79,60,59,54,72,77,78,84,76,78,68],"age_min":39,"age_max":84,"age_mean":65.09722222222223,"species":null,"sex_distribution":{"m":85,"f":59},"handedness_distribution":null},"experimental_modalities":null,"external_links":{"source_url":"https://openneuro.org/datasets/ds007526","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"163315aa4e514bd50050f9d082a28e09a23335ae91c918c997fb809c84e7d5b0","license":"CC0","n_contributing_labs":null,"name":"PD-EEG: Resting-State & Walking EEG in Parkinson's Disease","readme":"# PD-EEG: Resting-State & Walking EEG in Parkinson's Disease\n## Overview\nThis dataset contains EEG recordings from Parkinson's disease (PD) patients and healthy controls (HC), collected under two behavioral conditions: resting state (sitting) and walking. The dataset was acquired at the Neurology Institute, Tel Aviv Sourasky Medical Center.\n---\n## Participants\n- **Parkinson’s disease (PD):** 116 participants\n- **Healthy controls (HC):** 28 participants\n### Inclusion criteria (PD):\n- Age 40–90\n- Hoehn & Yahr stage ≤ 3\n- MoCA ≥ 21\n- Able to walk independently\n### Exclusion criteria:\n- History of stroke or major neurological disorder\n- Brain surgery\n- Significant head injury\n- Inability to walk independently\nAll participants provided informed consent. The study was approved by the local ethics committee and conducted in accordance with the Declaration of Helsinki.\n---\n## Experimental Design\nEach participant underwent EEG recording under two conditions:\n1. **Resting State** (144 Recordings)\n   - Sitting\n   - Eyes open\n   - Duration: ~4 minutes\n2. **Walking** (133 Recordings)\n   - Walking on a treadmill at a comfortable speed while holding the handrails.\n   - Duration: ~4 minutes\nAdditional clinical data were collected, including:\n- **Demographic data**\n- **LEDD** (Levodopa Equivalent Daily Dose) - a measure of anti-parkinsonian medication dosage.\n- **MoCA** (Montreal Cognitive Assessment) - a global measure of cognitive function.\n- **MDS-UPDRS**  - Movement Disorder Society Unified Parkinson's Disease Rating Scale - the gold standard clinical rating scale for Parkinson's Disease.\n- **CTT** - Color Trails Test - a measure of executive function and processing speed.\n---\n## EEG Acquisition\n- **System:** 64-channel Geodesic EEG System 400 (EGI system)\n- **Montage:** International 10–20 system\n---\n## Data Organization\nThis dataset follows the **Brain Imaging Data Structure (BIDS)** specification.\nTypical structure:\n```\nsub-001/\n    eeg/\n        sub-001_task-rest_eeg.*\n        sub-001_task-walk_eeg.*\nparticipants.tsv\nparticipants.json\ndataset_description.json\n```\n**Inbal Maidan, PhD**\nTel Aviv Sourasky Medical Center\nEmail: inbalm@tlvmc.gov.il","recording_modality":["eeg"],"senior_author":"Inbal Maidan (inbalm@tlvmc.gov.il)","sessions":[],"size_bytes":4578749719,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007526","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["rest","walk"],"timestamps":{"digested_at":"2026-04-22T12:30:31.226861+00:00","dataset_created_at":"2026-03-17T15:38:16.702Z","dataset_modified_at":"2026-03-18T08:13:34.000Z"},"total_files":277,"computed_title":"PD-EEG: Resting-State & Walking EEG in Parkinson's Disease","nchans_counts":[{"val":65,"count":277}],"sfreq_counts":[{"val":250.0,"count":277}],"stats_computed_at":"2026-04-22T23:16:00.312887+00:00","total_duration_s":70158.152,"tagger_meta":{"config_hash":"3557b68bca409f28","metadata_hash":"506b7df5f0b6810d","model":"openai/gpt-5.2","tagged_at":"2026-04-07T09:32:40.872789+00:00"},"tags":{"pathology":["Parkinson's"],"modality":["Motor"],"type":["Clinical/Intervention"],"confidence":{"pathology":0.9,"modality":0.75,"type":0.8},"reasoning":{"few_shot_analysis":"Most similar few-shot example by population is the Parkinson’s oddball dataset (\"Cross-modal Oddball Task.\") labeled Pathology=Parkinson's and Type=Clinical/Intervention, reflecting a large PD cohort with controls where PD-related cognitive/physiology is the main research focus. Another PD example (\"EEG: Reinforcement Learning in Parkinson's\") shows that Type can be non-clinical (Decision-making) when the paradigm is explicitly reinforcement learning/choice-focused. For the current dataset, the paradigm is resting + walking with extensive PD clinical scales, which aligns more with the clinical-cohort convention of the oddball PD example than with the decision-making PD example.","metadata_analysis":"Key metadata facts:\n- Clinical recruitment: \"This dataset contains EEG recordings from Parkinson's disease (PD) patients and healthy controls (HC)\" and \"Parkinson’s disease (PD): 116 participants\" (plus \"Healthy controls (HC): 28 participants\").\n- Two recording conditions: \"resting state (sitting) and walking.\" Also explicitly: \"Resting State ... Sitting ... Eyes open ... Duration: ~4 minutes\" and \"Walking ... Walking on a treadmill at a comfortable speed while holding the handrails.\"\n- Clinical/biomarker framing: \"Additional clinical data were collected, including: ... LEDD ... MoCA ... MDS-UPDRS ... CTT\" indicating a PD clinical characterization focus beyond a pure task-cognition experiment.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":"Pathology:\n- Metadata says: \"EEG recordings from Parkinson's disease (PD) patients and healthy controls (HC)\"; \"Parkinson’s disease (PD): 116 participants\".\n- Few-shot suggests: PD datasets should be labeled Pathology=Parkinson's.\n- Alignment: ALIGN.\n\nModality:\n- Metadata says: \"two behavioral conditions: resting state (sitting) and walking\"; \"Walking ... on a treadmill\".\n- Few-shot pattern suggests: paradigms centered on movement execution are typically treated with Motor as the dominant modality label (even if no external sensory stimulus is described), whereas pure eyes-open/closed with no task maps to Resting State.\n- Alignment: PARTIAL (dataset mixes rest and walking); choose the dominant non-rest active condition as Motor because walking is an explicit movement condition and is half of the dataset.\n\nType:\n- Metadata says: large clinical cohort and clinical metrics: \"Parkinson’s disease (PD): 116 participants\" and \"Additional clinical data were collected, including: ... LEDD ... MoCA ... MDS-UPDRS ...\".\n- Few-shot suggests: when pathology is the main focus in a large clinical cohort, Type=Clinical/Intervention (as in the PD oddball example), rather than a specific cognitive construct like Decision-making.\n- Alignment: ALIGN (clinical-cohort + biomarker/clinical characterization framing outweighs a purely motor-control experimental aim).","decision_summary":"Top-2 candidates per category (with head-to-head selection):\n\nPathology:\n1) Parkinson's — Evidence: \"Parkinson's disease (PD) patients\"; \"Parkinson’s disease (PD): 116 participants\"; title includes \"Parkinson's Disease\". Few-shot PD examples use Parkinson's.\n2) Healthy — Evidence: presence of \"Healthy controls (HC): 28 participants\".\nWinner: Parkinson's (PD is explicitly the clinical recruitment group and the dataset is PD-focused).\n\nModality:\n1) Motor — Evidence: \"walking\" condition; \"Walking on a treadmill\"; recording during active movement.\n2) Resting State — Evidence: \"Resting State ... Sitting ... Eyes open\".\nWinner: Motor (dataset is explicitly split between rest and walking; walking is a major condition and best captures the non-rest experimental input/behavioral context).\n\nType:\n1) Clinical/Intervention — Evidence: large PD cohort (\"116 participants\") plus clinical scales/medication dose (\"LEDD\", \"MDS-UPDRS\", \"MoCA\", \"CTT\"), consistent with clinical characterization/biomarker use.\n2) Motor — Evidence: explicit gait/walking paradigm (treadmill walking EEG).\nWinner: Clinical/Intervention (primary framing is PD vs control with extensive clinical phenotyping; motor walking appears as the behavioral probe within a clinical cohort study).\n\nConfidence justification:\n- Pathology high due to multiple explicit PD quotes + strong few-shot PD alignment.\n- Modality moderate-high because both rest and walking exist; Motor chosen as best single label capturing a major condition.\n- Type moderate-high because clinical-cohort framing is explicit, but Motor remains a plausible alternative given walking as a central condition."}},"canonical_name":null,"name_confidence":0.63,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"canonical","author_year":"Katzir2026"}}