Research Of The Complex Brain Effective Network

In recent years, the research of complex network theory which originated from the problem of seven bridge gets largely development. More and more characteristics of the network were found, lots of the algorithms for analyzing these characteristics have been proposed, and lots of methods for creating the complex network also were proposed. With the fast research of complex network, its application scope is more and more extensive, it has broken the traditional bondage of subjects, and becomes a transdisciplinary subject. It also is being used in biological sciences, brain science is the one of the two new frontier research of biological sciences. Human brain is a complex mega-system, electroencephalogram (EEG) signal as the external performance forms of human brain's behaviors, provides us for valuable information resources to study the complex giant system.The human brain is a highly complete functional system, the signal from the electrode of the data sampling system of EEG is not alone but connects with others. In other words, there exists information flow between this signals, we can call this information flow as causality. Nowadays, researchers detected the causality between variates in frequency domain, in this work,we realize the time domain conditional granger causality to detect this causality, we also compare this method with bivariate granger causality and partial directed coherence. We consider the electrode as a node, use the time domain conditional granger causality to detect the causality to form a weighted directed network (brain effective network), then we use the complex network theory to analyze the brain effective network which we get. We use time domain conditional granger causality to extract the different network of different forms based on the EEG data of epileptics, we use the complex network theory to analyze the different networks, then compare their characteristics.Overall, the time domain conditional granger causality can effectively extract effective network. By analyzing the brain effective networks we get, we found that there exist many obvious differences between the brain effective networks exacted from seizures interphase and episode patients.