Optimal Control Strategy for Epileptic Activity Based on Neural Mass Model

This study developed a closed-loop epilepsy control strategy utilizing a Neural Mass Model (NMM) to simulate epileptic seizures. The Strong Tracking Adaptive Unscented Kalman Filter (STAUKF) was employed to estimate the states and key parameters of the NMM, followed by the implementation of an optimal controller to modulate the epileptic state into a normal state. The entire closed-loop system comprises an NMM for simulating epileptic seizures, the STAUKF for estimating NMM parameters and states, local linearization of the model, and the designed optimal controller for epilepsy modulation.