Application of Horizontal Visibility Graph as a Robust Measure of Neurophysiological Signals Synchrony

Abstract

Finding synchronization between the signals of a neural system such as the brain (as a dynamic chaotic system) has a critical role in characterizing the system activities and integration of information within and across a disorder. Therefore, applying a synchronization measure, which is able to capture the important features of the system, has a great effect on the results. In this paper, we propose the horizontal visibility graph (HVG) method as a new, straightforward and fast method to measure synchronization between brain signals. By using the HVG method, a signal is mapped into a graph network and the signals properties are captured well. Our analysis shows that the proposed method is a robust and reliable measure for finding synchronization between signals of a dynamic chaotic (non-)noisy system. As examples for real life applications, the HVG method is used to observe Multiple Sclerosis (MS) brain network by using brain EEG signals. The results are in line with previous neurophysiology studies. Therefore, the proposed method could be a promising choice for analyzing real neural signals to characterize the system connectivity.