RAGMO-NET: REGIME-AWARE GRAPH MEMORY OPTIMIZATION NETWORK FOR EEG

Dynamic graph representation methods have shown promise for modeling electroencephalography (EEG) connectivity, but existing approaches are limited by heuristic memory updates, costly matrix-valued recurrent state transitions, and a lack of principled adaptation under non-stationary dynamics. We propose Regime-Aware Graph Memory Optimization Network (RAGMO-Net), a framework that adapts graph memory updates based on latent temporal regimes corresponding to evolving connectivity states. RAGMO-Net introduces a regime-conditioned affine graph-memory cell whose state transition is a scalar multiple of the identity, giving a constant-cost update per graph snapshot without matrix-valued gates or nonlinear state transitions. To provide a principled foundation for memory adaptation, we further formulate memory refinement as a regime-aware proximal refinement process, linking dynamic graph learning with non-stationary optimization. Experiments on both EEG and non-EEG datasets demonstrate consistent improvement over strong dynamic graph baselines. These results show that combining regime awareness with optimization-driven memory adaptation yields a scalable and robust approach for learning from non-stationary dynamic graphs.