Zyphra Releases ZUNA1.1: An Apache 2.0 EEG Foundation Model With Variable-Length Inputs From 0.5 To 30 Seconds

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Zyphra Releases ZUNA1.1: An Apache 2.0 EEG Foundation Model With Variable-Length Inputs From 0.5 To 30 Seconds Most EEG foundation models only work on the clean, fixed-length slices they were trained on. Real recordings are messy — and Zyphra spent an entire release closing that gap. They released ZUNA1.1 — a 380M masked diffusion autoencoder for scalp EEG under Apache 2.0, which reconstructs, denoises, and upsamples across arbitrary channel layouts. The architecture is near