کنفرانس مهندسی برق ایران

صفحه اصلی / سی و چهارمین کنفرانس بین المللی مهندسی برق

Seizure Suppression in a Neural Mass Model via Input-Output Feedback Linearization and Backstepping Control

نویسندگان :

Mahdi Moltamesi¹ Hasan Keshtkar²

1- دانشگاه تهران، دانشکده مهندسی برق و کامپیوتر 2- دانشگاه تهران، دانشکده مهندسی برق و کامپیوتر

کلمات کلیدی :

Epilepsy،Neural Mass Model،Backstepping Control،Feedback Linearization،Seizure Suppression،Nonlinear Control

چکیده :

Epilepsy is a complex dynamical disease characterized by the sudden onset of recurrent seizures, arising from hypersynchronous discharges within neuronal populations. For the substantial population of patients with drug-resistant epilepsy, neuromodulation techniques such as Deep Brain Stimulation (DBS) offer a promising therapeutic alternative. This paper proposes a robust nonlinear closed-loop control strategy to suppress epileptiform activity in a biologically inspired Neural Mass Model (Wendling model). We rigorously address the inherent nonlinearities of neuronal dynamics by employing Input-Output Feedback Linearization to transform the cortical column's dynamics into a linear equivalent form. Subsequently, a Backstepping controller is designed to force the system from a pathological limit cycle (ictal state) to track a reference trajectory representing normal background activity (interictal state). The control law is derived constructively based on Lyapunov stability theory, ensuring global asymptotic stability of the closed-loop system. Simulation results demonstrate that the proposed controller effectively restores normal neural activity by compensating for impaired dendritic inhibition, which is identified as the primary pathophysiological mechanism of seizure onset in this model.