Electroencephalograph analysis based on complex networks
基于复杂网络的脑电信号分析

This paper investigated one dimension EEG by converting it into complex networks via phase space reconstruction. To construct a complex networks, regarded each vector point as a node, and determined the edges by the phase space distance of each pair of vector points. A selective threshold value, which made the complex networks satisfy connectivity, could transform the distance matrix into a binary matrix. The binary matrix viewed as the adjacent matrix of complex networks was used to draw network topology and to analyze network characteristics. Applied the constructing networks method to distinguish EEG during eye-open and eye-closed resting conditions. The results indicate that recurrence plot, network topology, degree distribution and motifs distribution of the two networks show a distinct difference. Therefore complex networks as a data representation framework provide a new way for analyzing and distinguishing electroencephalograph.