Identifying important regions in EEG epilepsy brain networks.

Abstract

The human brain has been called the most complex object in the known universe and in many ways it constitutes the final frontier of science. Lately, the functional connectivity in human brain has been regarded and studied as a complex network using electroencephalography (EEG) signals. This means that the brain is studied as a connected system, where nodes represent different specialized brain regions and links or connections, represent communication pathways between the nodes. It is also fairly established that graph theory provides a variety of measures, methods and tools that can be useful to efficiently model, analyze and study an EEG network. In this article we study weighted and fully-connected brain networks, created from long-recorded EEG measurements that concern patients with focal and generalized epilepsy. We focus on the use of the well-known eigenvector centrality measure, which shows the influence of a node in a network and also constitutes the basis of the famous Google's PageRank algorithm. Our novel methodology reveals brain regions that might play a significant role before the occurrence of each epileptic seizure and also brain areas that might constitute the seed of the abnormal electrical activity that the human brain presents during epileptic seizures. Finally, we present and discuss the results and conclusions of our methodology, which demonstrates a standard EEG behavior in particular phases of the recording period.