nm000195 NEMAR-native dataset

Mixture of LLP and EM for a visual matrix speller (ERP) dataset from

This dataset comprises EEG recordings from a P300 visual matrix speller study comparing three unsupervised learning methods (Expectation-Maximization, Learning from Label Proportions, and their combination MIX) for brain-computer interface decoding. Twelve healthy participants performed a copy-spelling task using a modified 6×6 character grid (36 characters + 10 blank symbols = 46 total symbols) with flexible highlighting schemes, recorded at 1000 Hz from 31 EEG channels. The study demonstrates that unsupervised learning methods can achieve performance comparable to supervised approaches without requiring calibration data.

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/nm000195), 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 nm000195
cd nm000195 && nemar dataset get
```
Just the files.
rclone, aria2c, or any HTTPS client works against data.nemar.org/nm000195/ — 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.nm000195

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Files

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