Abstract
In this paper, electroencephalography data are used to establish a functional network connecting correlated human brain regions. Through analysis, it is found that the resulting network shows statistical characteristics of a complex network: its clustering coefficient is orders of magnitude larger than that of the equivalent random network, which is typical of a small-world network, and the distribution of degree is close to that of a scale-free network. All these characteristics reflect important functional information about brain states. For alcohol addicts, the characteristic indices of their brains are obviously different from those of the control group. The information entropy and standard information entropy of the brain neural network are also defined to measure the characteristics of the complex network. This gives a new criterion for clinical diagnosis and treatment of encephalopathy. Calculation results indicate that the brain neural network information entropy of alcohol addicts is quite distinct from that of the control group.