ZUNA
BCI foundation models for thought-to-text
ZUNA EEG Autoencoder
A generative model for reconstructing and enhancing EEG signals.
Modalities
Electroencephalography (EEG)
Brain Computer Interface (BCI)
Architecture
380M Parameters Diffusion Autoencoder
Features
Montage-Agnostic Scaling
Works across any EEG channel configuration.
Hardware-efficient gains
Improves signal quality and downstream performance.
Cross-dataset generalization
Generalizes across acquisition systems.
Real-world EEG ready
Designed for noisy, real-world EEG data.
Reconstruction + upsampling
Generalizes across acquisition systems.
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