{"success":true,"database":"eegdash","data":{"_id":"69de6d29897a7725c6702355","dataset_id":"nm000226","associated_paper_doi":null,"authors":["Bangyan Zhou","Xiaopei Wu","Zongtan Lv","Lei Zhang","Xiaojin Guo"],"bids_version":"1.9.0","canonical_name":null,"contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"10.82901/nemar.nm000115","datatypes":["eeg"],"demographics":{"subjects_count":4,"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://nemar.org/dataexplorer/detail/nm000226","osf_url":null,"github_url":null,"paper_url":null},"funding":[],"ingestion_fingerprint":"a2efba24118de5da7ceccee271dff57413ebe82c1e23e5213c2bc9b4914db8c2","license":"CC-BY-4.0","n_contributing_labs":null,"name":"Zhou2016","readme":"## Data Availability and Regeneration Instructions\nThis is a derivative dataset. If any data are missing, you can use the\ninstructions in the code folder to download the raw data and regenerate\nthe derivatives.\nREADME\nIntroduction\n------------\nThis dataset contains EEG recordings from four subjects performing motor imagery tasks (left hand, right hand, and feet), originally published by Zhou et al. (2016). The data was reformatted into BIDS from its Zenodo version (https://zenodo.org/records/16534752), which was itself generated by MOABB (Mother of All BCI Benchmarks, https://github.com/NeuroTechX/moabb). The original study investigated a fully automated trial selection method for optimization of motor imagery based brain-computer interfaces.\nOverview of the experiment\n--------------------------\nFour participants each completed three recording sessions separated by days to months. Each session contained two consecutive runs with inter-run breaks. Each run comprised 75 trials (25 per class: left hand, right hand, and feet imagery), for a total of 450 trials per subject across all sessions. Trials began with an auditory cue, followed by a 5-second visual arrow stimulus indicating the motor imagery task to perform, then a 4-second rest period. EEG was recorded from 14 channels placed according to the extended 10/20 system (Fp1, Fp2, FC3, FCz, FC4, C3, Cz, C4, CP3, CPz, CP4, O1, Oz, O2) at a sampling frequency of 250 Hz with a 50 Hz power line frequency.\nDataset structure\n-----------------\n- 4 subjects (sub-1 through sub-4)\n- 3 sessions per subject (ses-0, ses-1, ses-2)\n- 2 runs per session (run-0, run-1)\n- 24 EEG recordings total in EDF format\n- 14 EEG channels, 250 Hz sampling rate\n- 3 event types: left_hand (value=2), right_hand (value=3), feet (value=1)\n- Electrode positions in CapTrak coordinate system\nPreprocessing\n-------------\nThe data distributed here has undergone minimal preprocessing by MOABB prior to BIDS conversion:\n- Extraction of the 14 EEG channels from the original recordings\n- Annotation of motor imagery events (left_hand, right_hand, feet) with 5-second durations\n- Resampling to 250 Hz\n- Export to EDF format\nOriginal and related datasets\n-----------------------------\nThis dataset was reformatted into BIDS from the Zenodo archive at https://zenodo.org/records/16534752. That archive was generated by MOABB v1.2.0 from the original data accompanying the publication. The original study and data are described in:\nZhou B, Wu X, Lv Z, Zhang L, Guo X (2016). A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface. PLoS ONE 11(9): e0162657. https://doi.org/10.1371/journal.pone.0162657\nReferences\n----------\nZhou B, Wu X, Lv Z, Zhang L, Guo X (2016). A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface. PLoS ONE 11(9): e0162657. https://doi.org/10.1371/journal.pone.0162657\nAppelhoff S, Sanderson M, Brooks T, et al. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: 1896. https://doi.org/10.21105/joss.01896\nPernet CR, Appelhoff S, Gorgolewski KJ, et al. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8\nData curator for NEMAR version: Arnaud Delorme (UCSD, La Jolla, CA, USA)","recording_modality":["eeg"],"senior_author":null,"sessions":["0","1","2"],"size_bytes":553967593,"source":"nemar","storage":{"backend":"nemar","base":"s3://nemar/nm000226","raw_key":"dataset_description.json","dep_keys":["LICENSE","README.md","participants.json","participants.tsv"]},"study_design":null,"study_domain":null,"tasks":["imagery"],"timestamps":{"digested_at":"2026-04-30T14:09:13.071935+00:00","dataset_created_at":null,"dataset_modified_at":"2026-04-10T04:32:25Z"},"total_files":24,"author_year":"Zhou2016_226","name_source":"canonical","nchans_counts":[{"val":14,"count":24}],"sfreq_counts":[{"val":100.0,"count":24}],"computed_title":"Zhou2016","stats_computed_at":"2026-05-01T13:49:34.660289+00:00","total_duration_s":22575.760000000002}}