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nm000115 NEMAR-native dataset

Zhou2016

This dataset comprises EEG recordings from four subjects performing motor imagery tasks (left hand, right hand, and feet) across three sessions. The data includes 14-channel EEG recordings sampled at 250 Hz, with a total of 450 trials per subject. The dataset was originally collected to investigate automated trial selection methods for optimization of motor imagery-based brain-computer interfaces and has been reformatted into BIDS format.

EEG
Download zip Issues GitHub

Compute on this dataset

Two routes today, with a third (in-browser one-click submission) landing soon.

  1. NeuroScience Gateway (NSG) portal.

    NSG runs EEGLAB / Brainstorm / MNE pipelines on supercomputing time donated by SDSC. Create an account, point a job at this dataset's S3 prefix (s3://nemar/nm000115), and submit.
    nsgportal.org →

  2. Local processing with nemar-cli.

    Pull the dataset to your machine and run any toolbox locally. Honors the published version pinning. 

    npm install -g nemar-cli
nemar dataset clone nm000115
cd nm000115 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000115/ — the manifest carries presigned S3 URLs.

Direct compute access is coming soon. One-click NSG submission from this page is scoped for a follow-up phase. Tracked on nemarOrg/website#6.
DOI 10.82901/nemar.nm000115

![DOI](https://doi.org/10.82901/nemar.nm000115)

README

Introduction

This 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.

Overview of the experiment

Four 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.

Dataset structure

  • 4 subjects (sub-1 through sub-4)
  • 3 sessions per subject (ses-0, ses-1, ses-2)
  • 2 runs per session (run-0, run-1)
  • 24 EEG recordings total in EDF format
  • 14 EEG channels, 250 Hz sampling rate
  • 3 event types: lefthand (value=2), righthand (value=3), feet (value=1)
  • Electrode positions in CapTrak coordinate system

Preprocessing

The data distributed here has undergone minimal preprocessing by MOABB prior to BIDS conversion:

  • Extraction of the 14 EEG channels from the original recordings
  • Annotation of motor imagery events (lefthand, righthand, feet) with 5-second durations
  • Resampling to 250 Hz
  • Export to EDF format

Original and related datasets

This 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:

Zhou 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

References

Zhou 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

Appelhoff 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

Pernet 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

Data curator for NEMAR version: Arnaud Delorme (UCSD, La Jolla, CA, USA)

Files

8 top-level entries · 152 MB total