ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders

We introduce ZUNA, a 380M-parameter diffusion autoencoder with four-dimensional rotary positional embeddings for flexible EEG signal reconstruction and superresolution. We train two variants at scale: ZUNA1, which uses a typical, fixed dropout scheme, and ZUNA1.1, which introduces a a new set of masking strategies designed to reflect realistic EEG corruption and missing-data patterns beyond fixed patch reconstruction. ZUNA1.1 is capable of reconstructing variable length sequences of up to 30 seconds, 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. Among open-source models, ZUNA shows state-of-the-art reconstruction results across a range of dropout tasks.