Taming Epilepsy: Mean Field Control of Whole-Brain Dynamics
This addresses the problem of managing epileptic seizures for patients, representing a domain-specific advancement in medical control systems.
The paper tackled the challenge of controlling high-dimensional neural dynamics during epileptic seizures by proposing the Graph-Regularized Koopman Mean-Field Game (GK-MFG) framework, which achieved robust seizure suppression while respecting brain functional topology.
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.