Functional connectivity estimation aims to estimate the structure and properties of the network describing the dependencies between a number of locations in either sensor- or source-space. To estimate connectivity from M/EEG data, MNE-Python employs single-trial responses, which enables the detection of relationships between time series that are consistent across trials. Source-space connectivity estimation requires the use of an inverse method to obtain a source estimate for each trial. While computationally demanding, estimating connectivity in source-space has the advantage that the connectivity can be more readily related to the underlying anatomy, which is difficult in the sensor space.
