On the use of EEG functional connectivity networks in epilepsy studies

Brain network analysis represents an effective tool useful to explore the connectivity patterns to uncover related features and phenomena concerning different brain functions and diseases, such as the epilepsy. According to the clinical-psychological and neurological studies, signal patterns for network analysis mainly include data coming from functional magnetic resonance imaging (fMRI), positron emission tomography (PET), magnetoencephalogram (MEG), and electroencephalogram (EEG). High spatial-resolution fMRI and high temporal-resolution EEG are non-invasive and common techniques. EEG is quite low cost and, in many cases, EEG signal can provide important physiological information about the activity of the human brain which can be used to identify and detect epilepsy. In this paper we propose a framework for graph analysis of EEG functional connectivity networks, using EEG of epileptic subjects to build complex networks. Our aim is to provide a method to identify epilepsy in the early stages supporting physician in performing appropriate measures to prevent accidental consequences and to ensure health patient.