Analysis and Control of Epileptiform Spikes in a Class of Neural Mass Models

Abstract

The problem of analyzing and controlling epileptiform spikes in a class of neural mass models is concerned with. Since the measured signals are always contaminated by measurement noise, an algebraic estimation method is utilized to observe the state from the noisy measurement. The feedback control is constructed via the estimated state. The feasibility of using such a strategy to control epileptiform spikes in a regular network of coupled neural populations is demonstrated by simulations. In addition, the influence of the type of the controlled populations, the number of the controlled populations, and the control gain is investigated in details.