Documentation overview

Note

If you haven’t already installed Python and MNE-Python, here are the installation instructions, and some resources for Getting started with Python.

The documentation for MNE-Python is divided into four main sections:

1. 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.

2. The Examples Gallery 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.

3. 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.

4. 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 linked here:

• Frequently Asked Questions (FAQ)
• MNE-Python design philosophy
• Algorithms and other implementation details
• What’s new
• Roadmap
• Datasets Overview
• The typical M/EEG workflow
• Command line tools using Python
• Migrating from other analysis software
• How to cite MNE-Python
• Getting help
• General bibliography

Documentation for the related C and MATLAB tools are available here:

• MNE-MATLAB (HTML)

• MNE-C (PDF)