Overview of the MNE tools suite

MNE-Python is an open-source Python module for processing, analysis, and visualization of functional neuroimaging data (EEG, MEG, sEEG, ECoG, and fNIRS). There are several related or interoperable software packages that you may also want to install, depending on your analysis needs.

Related software

• MNE-C was the initial stage of this project, providing a set of interrelated command-line and GUI programs focused on computing cortically constrained Minimum Norm Estimates from MEG and EEG data. These tools were written in C by Matti Hämäläinen, and are documented here. See Installing MNE-C for installation instructions.

• MNE-Python reimplements the functionality of MNE-C, extends considerably the analysis and visualization capabilities, and adds support for additional data types like functional near-infrared spectroscopy (fNIRS). MNE-Python is collaboratively developed and has more than 200 contributors.

• MNE MATLAB provides a MATLAB interface to the .fif file format and other MNE data structures, and provides example MATLAB implementations of some of the core analysis functionality of MNE-C. It is distributed alongside MNE-C, and can also be downloaded from the MNE-MATLAB git repository.

• MNE-CPP provides core MNE functionality implemented in C++ and is primarily intended for embedded and real-time applications.

There is also a growing ecosystem of other Python packages that work alongside MNE-Python, including packages for:

Something missing?

If you know of a package that is related but not listed here, feel free to make a pull request to add it to this list.

• a graphical user interface for MNE-Python (MNELAB)

• easily importing MEG data from the Human Connectome Project for use with MNE-Python (MNE-HCP)

• managing MNE projects so that they comply with the Brain Imaging Data Structure specification (MNE-BIDS)

• automatic bad channel detection and interpolation (autoreject)

• convolutional sparse dictionary learning and waveform shape estimation (alphaCSC)

• independent component analysis (ICA) with good performance on real data (PICARD)

• phase-amplitude coupling (pactools)

• representational similarity analysis (rsa)

• microstate analysis (microstate)

• connectivity analysis using dynamic imaging of coherent sources (DICS) (conpy)

• general-purpose statistical analysis of M/EEG data (eelbrain)

• post-hoc modification of linear models (posthoc)

• a python implementation of the Preprocessing Pipeline (PREP) for EEG data (pyprep)

• automatic multi-dipole localization and uncertainty quantification with the Bayesian algorithm SESAME (sesameeg)

• GLM and group level analysis of near-infrared spectroscopy data (mne-nirs)

• M/EEG inverse solutions using artificial neural networks (ESINet)

What should I install?

If you intend only to perform ERP, ERF, or other sensor-level analyses, MNE-Python is all you need. If you prefer to work with shell scripts and the Unix command line, or prefer MATLAB over Python, probably all you need is MNE-C — the MNE MATLAB toolbox is distributed with it — although note that the C tools and the MATLAB toolbox are less actively developed than the MNE-Python module, and hence are considerably less feature-complete.

If you want to transform sensor recordings into estimates of localized brain activity, you will need MNE-Python, plus FreeSurfer to convert structural MRI scans into models of the scalp, inner/outer skull, and cortical surfaces (specifically, for command-line functions mne flash_bem, mne watershed_bem, and mne make_scalp_surfaces).

Getting help

Help with installation is available through the MNE Forum. See the Getting help page for more information.

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