CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models
2026-05-27 • Artificial Intelligence
Artificial IntelligenceHuman-Computer InteractionMachine Learning
AI summaryⓘ
The authors developed CaMBRAIN, a new model that can analyze brain wave data (EEG) in real time more efficiently than previous methods. Unlike older models that struggle with long recordings due to slow scaling and fixed input sizes, CaMBRAIN uses a special state space approach designed to handle long sequences quickly and continuously. They also created a new training method to help the model remember important brain activity patterns that are brief but spaced far apart. This approach led to better results on multiple EEG datasets while running over ten times faster than existing solutions.
Electroencephalography (EEG)State Space Models (SSM)Attention MechanismSelf-Supervised LearningCausal ModelingReal-Time InferenceLong-Range MemorySliding WindowSignal ReconstructionThroughput
Authors
Abhilash Durgam, Nyle Siddiqui, Jeffrey A. Chan-Santiago, Qiushi Fu, Elakkat D. Gireesh, Mubarak Shah
Abstract
Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to multiple hours, posing a major hurdle for existing deep learning methods due to two major factors: (1) existing EEG models are predominantly built upon the attention mechanism, incurring quadratic scaling as the sequence length increases, and (2) raw EEG signals must be processed in a sliding-window fashion due to fixed-length input requirements, preventing global understanding of the entire signal. To this extent, we propose CaMBRAIN - the first Causal, Mamba-based state space model (SSM) capable of real-time inference of EEG signals, arguing that bidirectional approaches are needlessly expensive given the causal, unidirectional nature of EEG. However, training such a model is non-trivial, as crucial EEG events can be extremely brief - within fractions of a second - yet separated by long intervals spanning minutes. Current EEG methods use self-supervised objectives that optimize for signal reconstruction, but these are not well suited for streaming SSMs; they fail to explicitly train the hidden state to retain the salient long-range context needed for streaming inference. We therefore introduce a multi-stage self-supervised training pipeline specifically tailored to encourage long-range memory retention and strong performance on EEG signals, while preserving the linear-time complexity of state space models. CaMBRAIN achieves state-of-the-art (SOTA) results across 3 different EEG datasets with >10x higher throughput than existing models, enabling the first model capable of long-range, continuous inference of variable-length EEG signals.