Overview of the MNE tools suite#

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:

• 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)

• All-Resolutions Inference (ARI) for statistically valid circular inference and effect localization (MNE-ARI)

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.