ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution

Title:ZUNA1.1: A more flexible EEG foundation model for Denoising and Super-resolution

Abstract:We introduce ZUNA1.1, a 380M-parameter diffusion autoencoder for flexible EEG signal reconstruction. ZUNA1.1 is capable of reconstructing variable length sequences of up to 30s, with an arbitrary number of EEG channels at arbitrary scalp locations, and can reconstruct arbitrary temporal intervals within channels in addition to reconstructing entire channels. We demonstrate that ZUNA1.1 performs at least on par with our earlier ZUNA1 model, while being far more flexible and capable of handling a wide range of reconstruction tasks. ZUNA1.1 continues to substantially outperform standard EEG denoising and reconstruction methods such as spherical spline interpolation, which is ubiquitously deployed in the MNE package. The ZUNA1.1 model is released open source under the permissive Apache 2.0 license.

Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)

arXiv:2607.27308 [cs.LG]

(or arXiv:2607.27308v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.27308 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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