The documentation for MNE-Python is divided into four main sections:
The Tutorials provide narrative explanations, sample code, and expected output for the most common MNE-Python analysis tasks. The emphasis is on thorough explanations that get new users up to speed quickly, at the expense of covering only a limited number of topics.
The How-to Examples provides working code samples demonstrating various analysis and visualization techniques. These examples often lack the narrative explanations seen in the tutorials, but can be a useful way to discover new analysis or plotting ideas, or to see how a particular technique you’ve read about can be applied using MNE-Python.
The Glossary provides short definitions of MNE-Python-specific vocabulary and general neuroimaging concepts. The glossary is often a good place to look if you don’t understand a term or acronym used somewhere else in the documentation.
The API reference provides documentation for the classes, functions and methods in the MNE-Python codebase. This is the same information that is rendered when running
help(mne.<function_name>)in an interactive Python session, or when typing
mne.<function_name>?in an IPython session or Jupyter notebook.
The rest of the MNE-Python documentation pages (parts outside of the four categories above) are shown in the navigation menu, including the list of example datasets, implementation details, and more. Documentation for the related C and MATLAB tools are available here: