Zyphra Releases ZUNA1.1: An Apache 2.0 EEG Foundation Model With Variable-Length Inputs From 0.5 To 30 Seconds
MarkTechPost Asif Razzaq
Zyphra released ZUNA1.1, an open-source EEG foundation model that reconstructs, denoises, and upsamples brain signals across variable channel layouts and recording lengths. The model accepts input lengths from 0.5 to 30 seconds (compared to ZUNA1's fixed 5-second segments) and uses 4D rotary positional encoding to handle arbitrary electrode configurations. The training expanded from 2 million to 3.5 million channel-hours of EEG data and introduced four dropout patterns instead of one, enabling better performance on realistic reconstruction tasks like region-based electrode recovery.
Why it matters
Zyphra released ZUNA1.1 on July 16, 2026, under the Apache 2.0 license. The 380M masked diffusion autoencoder reconstructs, denoises, and upsamples scalp-EEG across arbitrary channel layouts. It accepts variable-length inputs from 0.5 to 30 seconds, against ZUNA1's fixed five seconds. Reported NMSE holds or improves while the input range widens. The post Zyphra Releases ZUNA1.1: An Apache 2.0 EEG Foundation Model With Variable-Length Inputs From 0.5 To 30 Seconds appeared first on MarkTechPost.
