ZUNA: Flexible EEG Superresolution with Position-Aware Diffusion Autoencoders

We present $\texttt{ZUNA}$, a 380M-parameter masked diffusion autoencoder trained to perform masked channel infilling and superresolution for arbitrary electrode numbers and positions in EEG signals. The $\texttt{ZUNA}$ architecture tokenizes multichannel EEG into short temporal windows and injects spatiotemporal structure via a 4D rotary positional encoding enabling inference on arbitrary channel subsets and positions. We train ZUNA on an aggregated and harmonized corpus spanning 208 public datasets containing 2 million channel-hours using a combined reconstruction and heavy channel-dropout objective. We show that $\texttt{ZUNA}$ substantially improves over ubiquitous spherical-spline interpolation methods, with the gap widening at higher dropout rates. Crucially, compared to other deep learning methods in this space, $\texttt{ZUNA}$'s performance \emph{generalizes} across datasets and channel positions allowing it to be applied directly to novel datasets and problems.