MNE-Python is a scripting-based package with many visualization capabilities for visualizing results of processing steps and final outputs, but limited graphical user interfaces (GUIs) for actually performing processing steps. Leveraging the good readability on the Python language, particular care has been taken to keep the scripts simple, easy to read and to write. This is similar in spirit with the FieldTrip package (Oostenveld et al., 2011), in that it pushes users toward standardizing analyses via scripting instead of processing data in a GUI. Perhaps the largest downside of this scripting approach is that users clearly need to be able to write reasonable scripts. However this approach, which is facilitated by many examples that can be copied from the MNE website, has very clear benefits. First, our experience from analyzing several M/EEG studies unambiguously indicates that the processing pipeline must be tailored for each study based on the equipment used, the nature of the experiment, and the hypotheses under test. Even though most pipelines follow the same general logic (filtering, epoching, averaging, etc.), the number of options is large even for
