I. Introduction
With the advance of neuroimaging technology, such as EEG, it is possible to record brain activity with higher temporal resolution and accuracy than ever before. In order to understand the functioning of the brain better, methods to identify the communities or functional modules from the observed multichannel, multiple subjects recordings have been developed. Functional and effective connectivity are two widely studied measures to quantify the connectivity patterns in the brain. Unlike functional connectivity which only quantifies the statistical dependencies between two processes, effective connectivity quantifies the influence one node exerts on another node. Traditionally, effective connectivity has been quantified using measures of causality, such as Granger causality and partial directed coherence (PDC) [1]. Granger causality based methods are model dependent and limited to detecting the linear relations, however, EEG recordings are known to have nonlinear dependencies between recordings from different sites. Therefore, in our previous work, directed information was proposed to quantify the information flow in the brain network [2]. Unlike Granger causality, directed information is model free and can quantify the nonlinear relations.