Identifying multivariate EEG synchronization networks through multiple subject community detection

In neurophysiological studies, it is important to infer the functional networks underlying the observed physiological data. In recent years, measures of functional connectivity as well as tools from graph theory have characterized the human brain as a complex network composed of segregated modules linked by short path lengths. However, the current studies of functional connectivity focus on either solely quantifying the pairwise relationships or describing the global characteristics of the network using graph theoretic metrics. In order to understand the multivariate relationships within the network, it is important to determine the functional modules underlying the complex networks. Moreover, the study of these functional networks is confounded by the fact that most neurophysiological studies consist of data collected from multiple subjects, thus, it is important to identify functional modules representative of all subjects. We propose a hierarchical consensus spectral clustering approach based on the Fiedler vector to address these issues. Furthermore, measures based on hypothesis testing and information theory are introduced for selecting the optimal modular structure. The proposed framework is applied to EEG data collected during a study of error-related negativity (ERN) to better understand the functional networks involved in cognitive control.