The connectivity module in MNE-Python supports a number of bivariate spectral connectivity measures, i.e., connectivity is estimated by analyzing pairs of time series, and the connectivity scores depend on the phase consistency across trials between the time series at a given frequency. Examples of such measures are coherence, imaginary coherence (Nolte et al., 2004), and phase-locking value (PLV) (Lachaux et al., 1999). The motivation for using imaginary coherence and related methods is that they discard or downweight the contributions of the real part of the cross spectrum and, therefore, zero-lag correlations, which can be largely a result of the spatial spread of the measured signal or source estimate distributions (Schoffelen and Gross, 2009). However, note that even though some methods can suppress the effects of the spatial spread, connectivity estimates should be interpreted with caution; due to the bivariate nature of the supported measures, there can be a large number of apparent connections due to a latent region connecting or driving two regions that both contribute to the measured data. Multivariate connectivity measures, such as partial coherence (Granger and Hatanaka,
