To visualize resting-state synchronization across frequency bands in large-scale functional networks, various methods of connectivity between pairs of electrodes have been applied to EEG recordings. Most studies of AD connectivity assessed coherence, a linear connectivity measure that depends on the magnitude or power of the signals, and suggested reduced connections in different brain networks [17], [38], [39]. A few studies explored EEG nonlinear connectivity using pair-wise electrode functional coupling or synchronization likelihood measures [12], [21], [22]. Nonlinear measures potentially offer higher sensitivity due to the generality of the dependence structures they are able to capture [40]. However, it is well known that EEG suffers from the problem of volume conduction or common sources, which gives rise to spurious correlations between time series recorded from neighboring electrodes [41]. Thus, in sensor-level connectivity, activity of an underlying source is detected instantaneously (zero-lag) by different scalp electrodes. This field spread severely limits the utility of connectivity measures computed directly between sensor recordings. Exact Low Resolution Electromagnetic Tomography (eLORETA) represents a feasible solution to this problem as it assesses neuronal interactions at the level
