standard bivariate methods have been already demonstrated [26]. In fact, they can efficiently detect and remove the statistical spurious links from the functional connectivity estimation, even if in most cases, like PDC, MVAR methods need a large amount of data to obtain a reliable connectivity pattern. The precision in the estimation of parameters using MVAR models requires an appropriate length of EEG recordings, as the number of such parameters substantially grows when the number of time series to be modelled (i.e. the number of nodes in the final network) increases. Other studies have already shown that the precision of the connectivity estimation by MVAR models is highly sensitive to the length of the gathered EEG data [27], [28]. In practical cases, in which a limited amount of EEG data are available, the size of the MVAR model does not allow to take into account more than 12–15 times series at a time. In the present study, given the amount of collected EEG data, a maximum of twelve cortical areas could be accurately modeled by the PDC.
