nm000323 NEMAR-native dataset

Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy

This dataset comprises EEG recordings from 54 healthy participants performing a P300-based brain-computer interface speller task, designed to investigate BCI illiteracy. The study includes 62 EEG channels and 4 EMG channels sampled at 1000 Hz, with participants completing offline training and online test phases using a 36-symbol row-column speller paradigm. The dataset achieved 96.7% accuracy with an 11.1% BCI illiteracy rate, providing a comprehensive resource for evaluating P300-based BCI performance and individual differences in BCI competence.

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Issues GitHub

Download this dataset

dataset 131.7 GB exceeds 100.0 GB archive limit; use direct download. Use one of the streaming methods below — all resumable. Full download guide →

  1. NEMAR CLI recommended

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

    nemar dataset download nm000323
  2. DataLad

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

    datalad clone https://github.com/nemarDatasets/nm000323 nm000323
    cd nm000323 && datalad get .
  3. git-annex

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

    git clone https://github.com/nemarDatasets/nm000323 nm000323
    cd nm000323 && git annex get .
  4. 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/nm000323/v1.0.4/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/nm000323), 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 nm000323
    cd nm000323 && nemar dataset get
  3. Just the files.

    rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000323/ — 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
    54
    Size
    132 GB
    Tasks
    p300

    License and terms

    License
    GPL-3.0
    Recommended citation
    Lee, M., Kwon, O., Kim, Y., Kim, H., Lee, Y., Williamson, J., Fazli, S., & Lee, S. (2026). Lee et al. 2019 (ERP) — EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (Version v1.0.4) [Data set]. NEMAR. https://doi.org/10.82901/nemar.nm000323

    Where the bytes are

    Latest version (always current)
    https://data.nemar.org/nm000323/latest/

    How to download

    The dataset
    nemar dataset download nm000323 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 nm000323 --subjects sub-01,02 Also filters by --sessions, --tasks, --runs, --datatypes, --include and --exclude.
    A subset, step 1
    nemar dataset clone nm000323 Clones git-annex pointers only; fetches no file content. Creates ./nm000323.
    A subset, step 2
    cd nm000323 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/nm000323/v1.0.4/participants.tsv A direct HTTPS fetch works for any single file.