nm000161 NEMAR-native dataset

BNCI 2024-001 Handwritten Character Classification dataset

This dataset comprises EEG recordings from 20 healthy participants performing motor imagery of handwritten letter production. Participants imagined writing ten letters (a, d, e, f, j, n, o, s, t, v) using their right index finger in response to visual cues. The study investigates character classification through direct EEG decoding and a two-step approach combining continuous kinematic reconstruction with letter classification, achieving 26.2% accuracy for ten-letter classification and 46.7% for five-letter classification.

EEG

Download zip Issues GitHub

Compute on this dataset

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

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/nm000161), and submit.
nsgportal.org →
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 nm000161
cd nm000161 && nemar dataset get
```
Just the files.
rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000161/ — 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.nm000161

Files

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