Brain-computer interfaces (BCIs) are finally having their ‘foundation model’ moment. Zyphra, a research lab focused on large-scale models, recently released ZUNA, a 380M-parameter foundation model specifically for EEG signals. ZUNA is a masked diffusion auto-encoder designed to perform channel infilling and super-resolution for any electrode layout. This release includes weights under an Apache-2.0 license. The model aims to advance noninvasive thought-to-text development by enhancing the processing of EEG data. With the increasing interest in AI applications in healthcare and communication, ZUNA’s capabilities in interpreting brain signals for text generation could have significant implications for the future. The development and open-sourcing of such models contribute to the democratization of cutting-edge AI technologies and foster innovation in the field. For researchers and developers working on BCI systems, ZUNA provides a valuable tool for exploring the potential of EEG data in various applications.