STST-JEPA brings JEPA style self supervised learning to EEG 🧠 Rather than reconstructing the full waveform or predicting deep encoder states, the model learns to predict masked tokenizer latent shallow-targets from an EMA tokenizer. A small reconstruction head keeps the learned representation tied to the original EEG signal. The model uses 30 second multichannel windows, spatiotemporal block masking, coordinate aware channel pooling for mixed montages, and a 24 layer Transformer encoder. The architecture is only part of the story. STST-JEPA was pretrained on 47,703 sessions, including 22,588 sessions collected with our proprietary 117 channel EEG headset. Owning the hardware lets us build a large, consistent, high density EEG dataset beyond the scale of typical public datasets 🦉 Blog post: stst-jepa.io/ Arxiv: arxiv.org/pdf/2607.06629 @RoySegal2 @YSvechinsky @TomerFekete