Controlling the high-dimensional neural dynamics during epileptic seizures remains a significant challenge due to the nonlinear characteristics and complex connectivity of the brain. In this paper, we propose a novel framework, namely Graph-Regularized Koopman Mean-Field Game (GK-MFG), which integrates Reservoir Computing (RC) for Koopman operator approximation with Alternating Population and Agent Control Network (APAC-Net) for solving distributional control problems. By embedding Electroencephalogram (EEG) dynamics into a linear latent space and imposing graph Laplacian constraints derived from the Phase Locking Value (PLV), our method achieves robust seizure suppression while respecting the functional topological structure of the brain.
@article{arxiv.2603.18035,
title = {Taming Epilepsy: Mean Field Control of Whole-Brain Dynamics},
author = {Ming Li and Ting Gao and Jingqiao Dua},
journal= {arXiv preprint arXiv:2603.18035},
year = {2026}
}