arXiv Machine Learning August 4, 2026 By Shantanu Sarkar, Jose L. Contreras-Vidal

nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI

Read the original on arXiv Machine Learning →

arXiv:2605. 14941v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio, which makes extraction of meaningful neural information challenging.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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