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

2026-07-29

AI Summary

This work addresses the limited flexibility of existing EEG denoising and super-resolution methods with respect to sequence length, number of channels, electrode placement, and temporal segments. To overcome these constraints, the authors propose ZUNA1.1, a 380-million-parameter diffusion autoencoder that, for the first time, enables unified reconstruction of EEG signals under arbitrary missing patterns across temporal, channel, and spatial dimensions. The model supports variable-length inputs (up to 30 seconds), arbitrary numbers of channels, flexible electrode configurations, and can recover data from any temporal segment within a channel. Experimental results demonstrate that ZUNA1.1 matches or exceeds the performance of ZUNA1 across diverse reconstruction tasks and significantly outperforms conventional approaches such as spherical spline interpolation, exhibiting exceptional capabilities in both denoising and super-resolution. The code is publicly available under the Apache 2.0 license.

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.

Problem

• EEG denoising
• super-resolution
• signal reconstruction
• flexible EEG modeling
• diffusion autoencoder

Innovation

• diffusion autoencoder
• EEG reconstruction
• flexible signal processing
• denoising
• super-resolution