BrainMamba: A Scalable EEG Foundation Model for Coupled Multi-Channel Spatiotemporal Evolution

Large-scale representation learning from heterogeneous electroencephalography (EEG) data is essential for building generalizable EEG foundation models across diverse brain states, subjects, and downstream tasks. However, building scalable EEG foundation models faces two fundamental challenges: (1) data heterogeneity, as diverse channel configurations, acquisition protocols, and task settings hinder unified large-scale pretraining and cross-dataset transfer; and (2) neural dynamics modeling, as existing backbones struggle to capture the coupled spatial interactions and temporal evolution intrinsic to multi-channel brain activity. To address these challenges, we propose BrainMamba, a scalable foundation model that formulates large-scale EEG modeling as structured neural state evolution. At its core, BrianMamba models multi-channel EEG as a coupled dynamical system, where latent neural states evolve over time through structured state-space transitions while being continuously shaped by cross-channel interactions. By jointly capturing temporal evolution and inter-channel coupling, this formulation enables coherent modeling of distributed brain dynamics over long temporal contexts. To further accommodate the substantial heterogeneity of EEG recordings, the state evolution is adaptively specialized through expert routing, allowing different neural patterns across subjects, tasks, and recording conditions to be captured within a unified backbone. This state-centric design naturally scales across to heterogeneous channel configurations and sequence lengths, providing a unified foundation for large-scale EEG pretraining. BrainMamba adopts a large-scale mask-reconstruction-based pretraining paradigm, where masked spatiotemporal segments are reconstructed to learn EEG contextual semantic representations. For evaluation, we propose a novel validation paradigm for EEG foundation models, comprising task-constrained pretraining, channel-consistent transfer, and xwself-evaluation, to rigorously evaluate model generalization and robustness. Extensive experiments on more than 10 datasets across emotion, cognition, and disorder benchmarks demonstrate that BrainMamba consistently outperforms existing EEG foundation models, exhibiting superior cross-scenario generalization and strong scalability, highlighting its potential for real-world deployment.