In addition to SSP, MNE supports identifying artifacts and latent components using temporal ICA. This method constitutes a latent variable model that estimates statistically independent sources, based on distribution criteria such as kurtosis or skewness. When applied to M/EEG data, artifacts can be removed by zeroing out the related independent components before inverse transforming the latent sources back into the measurement space. The ICA algorithm currently supported by MNE-Python is FastICA (Hyvärinen and Oja, 2000) implemented in Scikit-Learn (Pedregosa et al., 2011). Here, MNE-Python has added a domain specific set of convenience functions covering visualization, automated component selection, persistence as well as integration with the MNE-Python object system. ICA in MNE-Python is handled by the ICA class which allows one to fit an unmixing matrix on either Raw or Epochs by calling the related decompose_raw or decompose_epochs methods. After a model has been fitted, the resulting source time series can be visualized using trellis plots (Becker et al., 1996) (cf. Figure 6) as provided by the plot_sources_raw and plot_sources_epochs methods (illustrated in Figure 6). In addition, topographic plots depicting the
