Taming Epilepsy: Mean Field Control of Whole-Brain Dynamics

TL;DR

This paper introduces GK-MFG, a novel control framework combining reservoir computing and graph-regularized mean-field game theory to effectively suppress epileptic seizures by controlling high-dimensional brain dynamics.

Contribution

It presents a new method integrating Koopman operator approximation with graph constraints for seizure control, addressing nonlinear brain dynamics.

Findings

Robust seizure suppression demonstrated in EEG data

Effective embedding of brain dynamics into linear latent space

Incorporation of graph Laplacian constraints improves control accuracy

Abstract

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.