Overexcitation of neurons in brain can lead to epilepsy seizures, and the key to control epilepsy seizures is to keep the balance between excitation and inhibition. In this paper, epileptiform index is presented to denote the seizure degree and used as control variable of PID controller to control epilepsy seizures. Neural mass model (NMM) is used as a test-bed to simulate the change of seizure degree with the increase of excitatory strength and two control strategies. Experimental results showed that the increase of excitatory strength could lead to a substantial increase of epileptiform index and trigger seizures. PID controller which is used to decrease excitatory strength or increase inhibitory strength can keep excitation-inhibition balance and inhibit epilepsy seizures. Epileptiform index can describe the linear and nonlinear feature of electroencephalogram (EEG) comprehensively, and PID controller is simple and independent of underlying physiological structure, which lays the foundation for its application in the clinic.
Using the computer to imitate the neural oscillations of the brain is of great significance for the analysis of brain functions. Thalamocortical neural mass model (TNMM) reflects the mechanisms of neural activities by establishing the relationships between the thalamus and the cortex, which contributes to the understanding of some specific cognitive functions of the brain and the neural oscillations of electroencephalogram (EEG) rhythms. With the increasing complexity and scale of neural mass model, the performance of conventional computer system can not achieve rapid and large-scale model simulation. In order to solve this problem, we proposed a computing method based on Field Programmable Gate Array (FPGA) hardware in this study. The Altera's DSP Builder module combined with MATLAB/Simulink was used to achieve the construction of complex neural mass model algorithm, which is transplanted to the FPGA hardware platform. This method takes full advantage of the ability of parallel computing of FPGA to realize fast simulation of large-scale and complex neural mass models, which provides new solutions and ideas for computer implementation of neural mass models.