Research Of EEG Signal Based On Visibility Graph

The human brain can be regarded as a complex system with real existence.Researchers use complex network theory to explore the interaction of brain function intervals,the topological features of brain networks,and the pathological information reflected by abnormal brain waves.In order to find a valuable nonlinear processing method of EEG signals,this paper studies from three directions: EEG signal complexity,brain complex network degree distribution and eigenvalue,the relationship of Hurst exponent H of the EEG signal time series and its visibility graph network degree distribution power law index ?.This paper uses the Visibility Graph algorithm,the Horizontal Visibility Graph algorithm,and the Limited Penetrable Visibility Graph algorithm to systematically explore the normal EEG signals and the epileptic EEG signals(resting state),mainly divided into the following three research directions:The first is based on the EEG signal research of Visibility Graph and Horizontal Visibility Graph,used the Visibility Graph and Horizontal Visibility Graph algorithms to construct complex networks of normal EEG signals and epileptic EEG signals to realize the visualization of network topology.Meanwhile calculated and studied the characteristics of epileptic EEG signals and normal human EEG signals,including degree distribution,clustering coefficient and average path length.By comparing the network characteristics of the two groups of EEG signals,it is found that patients with epilepsy have a larger clustering coefficient and a smaller average path length,and the Visibility Graph algorithm can effectively distinguish the two groups of EEG signals,and the Horizontal Visibility Graph algorithm effect is not ideal.The second is based on the Limited Penetrable Visibility Graph algorithm to study EEG signals.The Limited Penetrable Visibility Graph algorithm is used to construct a complex network of normal EEG signals and epileptic EEG signals,and to visualize the network topology map.Meanwhile calculating and studying the characteristics of epileptic EEG signals and normal EEG signals,including degree distribution,clustering coefficient and average path length.By comparing the network characteristics of the two groups of EEG signals,it is found that patients with epilepsy have larger clustering coefficient and smaller average path length.The Limited Penetrable Visibility Graph algorithm can effectively distinguish the two groups of EEG signals.The experimental results verify that the Visibility Graph algorithm and the Limited Penetrable Visibility Graph algorithm can effectively analyze the brain functional network,And their experimental results arebetter than the Horizontal Visibility Graph algorithm,and also to further study the complex network of epileptic EEG signals.At the same time,it also lays a foundation for further study of the characteristics of the complex network of epileptic EEG signals.The third is based on the visibility graph network to study the linear relationship between the Hurst exponent H of the EEG signal time series and its visibility graph network degree distribution power law index ?.This paper verifies that both epilepsy EEG signals and normal EEG signals satisfy this linear relationship-(28)H? 25,and the Hurst exponents of the two groups of EEG signal time series are greater than 0.5,which is confirmed to be a continuous time series,indicating that the time series has long-term memory.The research in this paper is of great value for clinical brain function assessment and monitoring and detection of brain function rehabilitation.