Research of EEG Signal Based on Permutation Entropy and Limited Penetrable Visibility Graph

Complex networks can be seen as a way of describing complex systems. Starting from the end of the twentieth Century, the theory of complex network has gradually penetrated into all fields of social science, and it has become one of the most important tools for people to solve the problem. The complex network theory is helpful in studying the interaction between different brain regions, topology structure and the dynamic information, as well as the relationship between disease and physiological function. Electroencephalogram(EEG) is an important tool for the disease diagnosis and prediction. The paper adopts Permutation Entropy(PE) and Limited Penetrable Visibility Graph(LPVG) algorithm to construct the complex networks and implement networks visualization. Using this method to research 21 normal people and 21 epilepsy EEG signal, in addition compare statistical characteristics of different brain networks. The results verify the validity of the PE and LPVG algorithm for analyzing brain functional networks and show that the properties of the different attention EEG are different. This method provides important reference for further study of the brain function network dynamics of epileptic EEG signals.