State space models with causal (one-directional) processing are more efficient than attention-based models for streaming EEG analysis, and specialized self-supervised training can teach them to remember important brain events separated by long time gaps.
CaMBRAIN is a new AI model for analyzing brain activity from EEG signals in real-time. Unlike existing models that struggle with long recordings, it uses a causal state space architecture that processes signals sequentially without the computational overhead of attention mechanisms, enabling continuous analysis of hours-long EEG data while running 10x faster.