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 spatial sensitivities of the unmixing matrix are provided by the plot_topomap method (illustrated in Figure 6). Importantly, the find_sources_raw and find_sources_epochs methods allow for identifying sources based on bivariate measures, such as Pearson correlations with ECG recording, or simply based on univariate measures such as variance or kurtosis. The API, moreover, supports user-defined scoring measures. Identified source components can then be marked in the ICA object's exclude attribute and saved into a FIF file, together with the unmixing matrix and runtime information. This supports a sustainable, demand-driven workflow: neither sources nor cleaned data need to be saved, signals can be reconstructed from the saved ICA structure as required. For advanced use cases, sources can be exported as regular raw data or epochs objects, and saved into FIF files (sources_as_raw and sources_as_epochs). This allows any MNE-Python analysis to be performed on the ICA time series. A simplified ICA workflow for identifying, visualizing and removing cardiac artifacts is illustrated in Table 2.
