All the source estimation strategies presented thus far, from MNE to dSPM or beamformers, lead to linear transforms of sensor-space data to obtain source estimates. There are also multiple inverse approaches that yield non-linear source estimation procedures. Such methods have in common to promote spatially sparse estimates. In other words, source configurations consisting of a small set of dipoles are favored to explain the data. MNE-Python implements three of these approaches, namely mixed-norm estimates (MxNE) (Gramfort et al., 2012), time–frequency mixed-norm estimates (TF-MxNE) (Gramfort et al., 2013b) that regularize the estimates in a time–frequency representation of the source signals, and a sparse Bayesian learning technique named γ-MAP (Wipf and Nagarajan, 2009). Source localization results obtained on the ERF evoked by the left visual stimulus with both TF-MxNE and γ-MAP are presented in Figure 10.
