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arXiv Machine Learning By Abdulkader Helwan, Khadidja Henni, Lina Abou-Abbas

NOMAD-EEG: Native Omni-Montage Adaptive Diffusion

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NOMAD-EEG is a 12‑million‑parameter diffusion model that learns EEG representations independent of electrode number, identity, or recording length by using 3D scalp positions and state‑space layers over time. Trained on 147 h of unlabelled EEG from a single corpus, it outperformed seven larger foundation models across ten datasets, achieving superior accuracy on tasks such as seizure detection, epilepsy diagnosis, motor imagery, and P300 detection. The model’s compact size and single‑corpus training yet strong performance demonstrate the feasibility of a versatile, lightweight EEG foundation model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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