Epilepsy is a temporary event of symptoms caused by abnormal over-activation of neurons in brain, and about 65 million people worldwide suffer from it. The main symptom of the disease is seizure. The methods of treating the disease include medicine, surgery, and electrical and nerve stimulation. In the event that a person does not respond to medical treatment and the risk of surgery is high, electrical stimulation treatment is proposed. In this thesis, the computational models of the cortex and its relationship with the thalamus and the interpretation of epileptic activities are addressed, and after a brief description of each model, the proposed model (computational thalamocortical) is selected to study the dynamics of the seizure. Also, choosing the control algorithm is of paramount importance, so in this thesis we introduce and compare control algorithms, and the controller is designed in a way that it directs abnormal activities caused by epilepsies, looking for normal and desirable activities.The main objective of the thesis is to develop a fast robust control technique based on sliding mode control (SMC) in combination with adaptive neural network control to suppress the seizure in a computational thalamocortical model of epilepsy, and due to the impossibility of measuring some variables and uncertainty in the system, a radial basis function (RBF) neural network is used to estimate the upper bounds of the external disturbance and unknown nonlinear function of the plant to be controlled. A critical issue in controlling the nonlinear dynamical systems in the simulations of brain networks is eva‎luating the performance of the proposed controller in the presence of external disturbance in comparison with the previous works. The bounded stability of all signals in the closed-loop system is guaranteed with a Lyapunov function. To eva‎luate the accuracy of the proposed control scheme in tracking the desired trajectory (healthy state) the root mean square (RMS) of the tracking error is calculated. The simulation results show the effectiveness of the proposed control scheme compared to the previous works. The control input (continuous deep brain stimulation) is chattering-free without any singularity problem. Also, the performance of the controller is checked by applying the disturbance signal. Moreover, for the first time the performance of the proposed adaptive SMC in controlling the multi-compartment thalamocortical model is investigated.

مريم ذكري

مريم ذكري

مريم ذكري , جلال ذهبي