nm000242 NEMAR-native dataset

Visual imagery EEG dataset from Gao et al 2026

This dataset contains 32-channel EEG recordings from 22 healthy adults performing a visual imagery task involving 10 categories of animals, figures, and objects. Data were collected across two sessions per subject using a Neuracle NeuSenW32 system at 1000 Hz sampling rate, and are formatted according to BIDS for use in brain-computer interface research. The dataset supports evaluation of classification approaches such as CSP and EEGNet for decoding imagined visual categories from EEG signals.

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Issues GitHub

Download this dataset

Pick a method. Large datasets skip the zip and use the streaming methods below — all resumable. Full download guide →

  1. Download archive (.zip) — 43.0 GB

    A single zip of the published version. Best for small/medium datasets.

    Download zip

  2. NEMAR CLI recommended

    Pulls the pinned version + annexed data and resumes cleanly. Install nemar-cli →

    nemar dataset download nm000242
  3. DataLad

    Clone the dataset repo and fetch file content on demand. Docs →

    datalad clone https://github.com/nemarDatasets/nm000242 nm000242
    cd nm000242 && datalad get .
  4. git-annex

    Plain git + git-annex against the dataset repo. Docs →

    git clone https://github.com/nemarDatasets/nm000242 nm000242
    cd nm000242 && git annex get .
  5. Direct files (wget / curl / rclone)

    Every file with a stable, range-resumable URL from the manifest. Needs curl, jq, wget (or rclone/aria2c). Docs →

    curl -s https://data.nemar.org/nm000242/v1.0.3/manifest.json | jq -r '.[].bytes_url' > urls.txt
    wget -xc -i urls.txt

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/nm000242), 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 nm000242
    cd nm000242 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000242/ — 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.

Citations

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    Files

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    Signal viewer

    How to use the data (for agentic research) license, citation, download commands

    What it is

    Modalities
    EEG
    Participants
    22
    Size
    75.4 GB
    Tasks
    AVI, FVI, OVI, imagery

    License and terms

    License
    CC-BY-NC-ND-4.0
    Note
    No derivative works permitted (CC-BY-NC-ND-4.0).
    Recommended citation
    Gao, J., Liu, Y., Li, Z., Huang, K., Wang, F., Xu, J., Zhao, L., Li, T., & Fu, Y. (2026). Visual imagery EEG dataset from Gao et al 2026 (Version v1.0.3) [Data set]. NEMAR. https://doi.org/10.82901/nemar.nm000242

    Where the bytes are

    Latest version (always current)
    https://data.nemar.org/nm000242/latest/

    How to download

    The dataset
    nemar dataset download nm000242 Clones and fetches in one step. Content under stimuli/ and derivatives/ is skipped by default because those trees can be large; add --stimuli --derivatives for the whole thing.
    A subset, one step
    nemar dataset download nm000242 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
    A subset, step 1
    nemar dataset clone nm000242 Clones git-annex pointers only; fetches no file content. Creates ./nm000242.
    A subset, step 2
    cd nm000242 The get command below reads the clone's annex, so it only works from inside the clone.
    A subset, step 3
    nemar dataset get <files> Pulls the files you actually need. Skips stimuli/ and derivatives/ unless the path you ask for is under one of them.
    One small file
    https://data.nemar.org/nm000242/v1.0.3/participants.tsv A direct HTTPS fetch works for any single file.