Because the cognitive functions of the brain are fundamentally based on the coordinated interactions of neuronal sources across distributed brain regions, quantifying neural synchrony can provide evidence of functional integration in the brain. Recent neuroscience methodologies have utilized two types of indices for large-scale neural synchrony — linear and nonlinear measures (Pereda et al., 2005). Linear synchrony measures, such as temporal correlation, spectral coherence, and directed transfer function, have been widely used to quantify the degree of synchronization in the neural system. The assumption of the stationarity and linearity between brain activity from different regions limits how well linear measures can characterize pathological nonlinear neuronal synchrony as well as the intrinsic brain dynamics (Aviyente et al., 2011). On the other hand, nonlinear measures address this limitation by using measures of phase synchrony and generalized synchronization. In this study SL was used to detect the linear and nonlinear inter-dependences between two dynamical systems (Stam and van Dijk, 2002). However, some statistical measures for signal interdependencies can be spuriously biased by the volume conduction effect of source activities within the brain (Guevara
