Zyphra releases open EEG foundation model with flexible input length

Zyphra released ZUNA1.1 on July 16 under an Apache 2.0 license, a 380-million-parameter masked diffusion autoencoder for scalp EEG (electroencephalogram) signals. Unlike its predecessor's fixed five-second window, it accepts variable-length inputs from 0.5 to 30 seconds while reconstructing, denoising, and upsampling EEG across different channel layouts. Reported normalized mean squared error held steady or improved despite the wider input range.

Why it matters: Open, permissively licensed foundation models for biosignals like EEG lower the barrier for researchers and startups building brain-computer interfaces and clinical diagnostic tools without training from scratch. It's part of a broader pattern of foundation-model techniques spreading from text and images into specialized scientific and medical domains.