Study On The Synchronization Of Complex Brain Network Constructed With EEG Signal

Brain System is the most complex network in natural world. This character reflects not only in the sophisticated structure formed by billions of cortex neuron connecting in space but it gathering and making synergistic effect on emotional recognize function as well. Therefore, it is the top issue that using complex network theory to analyzing the coupling relationship contained in brain system. There is so many indicators which illustrate efficiency and working condition in complex network, such as mean distance 、 degree of coupling 、 clustering coefficient and degree distribution. Different indicators measure different characters.This paper discuss the reason of synchronization forming and characters of synchronized network from the perspective of complex network structure stability. We propose the synchronized model adapted to brain network according to the Lyapunov stability theory in complex network. Then we offer the theoretical foundation and whole proof and explore the theoretical criteria about how the brain network to be synchronized. After that,we use alcoholics EEG data and DEAP EEG data to certify the model we have proposed. And the article compared the difference, which contrast the result acquired in DEAP EEG data, of synchronized brain network characters between Alcoholics and normal. The main work are as follows:(1)Ameliorating the general complex network synchronization model to adapt with brain network. Eliminating imaginary part in the state parameter of final synchronization discriminator but add an extra variable. Using Block Coordinate Descent Algorithm to transfer the brain network synchronization model and making construction of Lyapunov discriminator. Proposing theoretical criteria about judging whether the brain network are synchronized.(2)Presenting an node selecting rules about how to creating a sub-network from the integrated brain network which are named as Random Apolo Method. And we analysis the sub-network’s synchronization status variation tendency as the changing of network size.(3)On Alcoholics EEG data, we make electrode as node and choose Synchronization- likelihood to measure the correlation between each node. Then we build up EEG functional brain network about Alcoholics and normal, contrast synchronous status duration, and the stability of the network when it comes into synchronous status. We draw a conclusion that normal brain network hold a longer duration than alcoholics and the status is more stable. Finally, we have dug the cause by doing ICA that there are some stronger sync capabilities nodes in normal brain network generate the synchronous status, just as the opposite is happening in alcoholics brain network.(4)We have done the same procedure on creating brain network in DEAP EEG data, and also analyzed synchronous status duration, the stability of the network, and the single node’s contributions to synchronous status to further proving the conclusion mentioned above.Generalized speaking, this article improve the complex network synchronization model to make it suitable on brain network, and proved the applicability by making experiments on two EEG database. The result shows that the model we have proposed works quietly well on brain network, and normal brain network takes more advantage in both synchronous status duration and stability than alcoholics. I hope to offer some useful references and new perspectives on brain network research.