Filtering And Control Of Neural Dynamical Model

Electroencephalogram(EEG)can record the brain rhythm which is the reaction of the state of brain function.The modulation of brain rhythm based on the electrical stimulation for the treatment of brain diseases has a positive clinical outcome.The neural dynamical model can simulate the activities of brain rhythm associated with different states of brain function.A research on the filtering and control of the neural dynamical model can provide the theoretical support for the acquisition of the information from the state of the abnormal brain function under the interference of the measure noise and the realization of the electrical stimulation in the treatment of brain diseases.In this paper,we consider that EEG suffers from the influence of the measurement noise in the recording process.So the filtering scheme is designed to reduce the noise effect based on the neural dynamical model.In addition,we also design the closed-loop control framework based on the neural dynamical model to adjust the dynamic characteristics.Specific tasks are as follows:Firstly,the filtering scheme based on the Cubature Kalman Filter(CKF)algorithm is applied to the neural dynamical model,and compares the filtering performance with the Unscented Kalman Filter(UKF)algorithm.Secondly,with respect to the mutational parameter from the neural dynamical model,the filtering scheme based on the Strong Tracking Cubature Kalman Filter(STCKF)algorithm is applied to the neural dynamical model,and compares the filtering performance with the CKF algorithm.Finally,for different control objectives,the control schemes respectively based on the CKF algorithm and the single neuron adaptive PID algorithm are designed with the closed-loop control framework to adjust the dynamic characteristics of the neural dynamical model.