LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling
Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity. As a joint solution, we propose LuMamba (Latent Unified Mamba), a self-supervised framework combining topology-invariant encodings with linear-complexity state-space modeling, using LUNA's learned-query cross-attention mechanism for channel unification, and FEMBA's bidirectional Mamba blocks for efficient temporal modeling. Within this architecture, we provide the first s...
| Authors | Danaé Broustail, Anna Tegon, Thorir Mar Ingolfsson et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 March 2026 |
| Last updated at source | 4 June 2026 |
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