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

Model With Variable-Length Inputs From 0.5 To 30 Seconds">

This week, Zyphra released ZUNA1.1 under the Apache 2.0 license . The EEG foundation model reconstructs, denoises, and upsamples data across arbitrary channel layouts. It builds on ZUNA1, the Zyphra’s earlier open EEG foundation model.

The main change is flexibility, not a jump in raw accuracy. Real EEG recordings are messy. Sessions vary in length, and channels go noisy or drop out mid-session. Montages range from four-electrode headbands to 256-channel research caps. ZUNA1 processed only fixed five-second segments. ZUNA1.1 accepts variable-length inputs from 0.5 to 30 seconds.

To understand that flexibility, start with what the model does.

ZUNA1.1 is a 380M-parameter masked diffusion autoencoder for scalp-EEG signals. Given a subset of channels, it denoises existing EEG segments and channels. It reconstructs missing ones. It also predicts novel channel signals given physical coordinates on the scalp.

The parameter count is unchanged from ZUNA1. It runs on a consumer GPU and works acceptably on CPU for many workloads. Weights sit on Hugging Face; inference and preprocessing code sit on GitHub. Install with pip install zuna . Zyphra also hosts a free browser EEG Playground, and ships all of this for research use only.

ZUNA is a transformer encoder–decoder diffusion autoencoder. It slices each channel into 0.125 second segments, which is 32 samples at 256 Hz. Each segment becomes a continuous-valued token. Tokens are serialized in channel × time order.

The positional encoding is the key idea. Each token carries a 4D rotary positional encoding over (x, y, z, t). That is the electrode’s 3D scalp coordinate along with its coarse-time index. Because position, not array index, tells the model where a channel sits, ZUNA is channel-agnostic. It accepts any electrode layout, and can generate signals at positions never recorded. That capability enables arbitrary channel upsampling by location.

The encoder compresses the signal into a latent. That latent conditions the decoder via adaptive-RMS norm. The decoder is trained with a rectified-flow objective. ZUNA1.1’s architectural changes targeted training stability, such as added normalization layers.

Since the architecture stayed close, the differences come from training.

1. Variable-length inputs (0.5–30 seconds) : ZUNA1.1 samples a segment length per training example, snapped to the 0.125 s token grid. Lengths are drawn across four bins, from very short to long. The middle 1.5–10 s range is oversampled, since it is the most common operating point. Because token counts vary, Zyphra packs multiple segments per batch up to a fixed budget. Flex attention with a sample-aware mask stops tokens attending across samples. One model therefore serves a 0.5 s snippet and a 30 s stretch without reconfiguration.