nm000346 NEMAR-native dataset

CastillosCVEP100

A derivative 4-class code-VEP brain-computer interface dataset processed from the original Zenodo dataset (10.5281/zenodo.8255618), comparing burst c-VEP and m-sequence stimulation paradigms at two amplitude depths (100% and 40%). The study evaluates classification performance and user experience using 32-channel EEG recorded at 500 Hz from 12 healthy participants. Burst c-VEP achieved superior accuracy of 95.6% (with 52.8s calibration) and 90.5% (with 17.6s calibration), compared to m-sequence performance of 85.0% and 71.4% respectively. CNN-based decoding with 250ms sliding windows enabled these results. This derivative dataset optimizes stimulus design for reactive BCI applications while maintaining visual comfort, with reduced amplitude (40%) showing minimal accuracy loss (94.2%) while substantially improving user experience.

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/nm000346), 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 nm000346
cd nm000346 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000346/ — 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.nm000346

Loading demographics…

Files

Loading file index…