ZUNA: EEG Foundation Model

ZUNA is a 380M-parameter masked diffusion autoencoder trained to reconstruct, denoise, and upsample scalp-EEG signals. Given a subset of EEG channels, ZUNA can:

• Denoise existing EEG channels
• Reconstruct missing EEG channels
• Predict novel channel signals given physical coordinates on the scalp

ZUNA was trained on approximately 2 million channel-hours of EEG data from a wide range of publicly available sources. At 380M parameters, it is lightweight enough to run on a consumer GPU and can be used on CPU for many workloads.

Performance

ZUNA significantly outperforms existing standard methods for channel denoising, reconstruction, and upsampling. We compared ZUNA to MNE's default spherical spline interpolation method. ZUNA outperforms MNE in reconstruction accuracy across a range of unseen datasets, even those with a different preprocessing pipeline. ZUNA's advantage is particularly striking for higher upsampling ratios, demonstrating that it is effectively using general priors learned through large-scale pretraining.

Getting Started

For installation, tutorials, and API documentation, see the GitHub repository.

A Google Colab notebook is also available here for free GPU access.

Citation

For more information please see our technical whitepaper and blog. If you find ZUNA useful in your work, please cite accordingly.

Organizations or researchers interested in collaborating with Zyphra to improve future versions for specific needs or use cases should contact bci@zyphra.com.

Disclaimer

This software and related services ("Services") are provided for research use only and are not intended for use in the diagnosis, cure, mitigation, treatment, or prevention of any disease or health condition. The Services have not been validated for any medical or clinical use. The information provided through the Services is for general informational purposes only and is not a substitute for any professional medical or healthcare advice. We do not warrant that any information provided through the Services is accurate, complete, or useful to you. Any reliance you place on such information is strictly at your own risk.