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 GitHub 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:

Note

Something missing?

If you know of a package that is related but not listed here, feel free to to add it to this list by making a pull request to update doc/sphinxext/related_software.py.

• alphaCSC: Convolutional dictionary learning for noisy signals.

• antio: Python package to handle I/O with the CNT format from ANT Neuro.

• autoreject: Automated rejection and repair of epochs in M/EEG.

• best-python: The goal of this project is to provide a way to use the best-brainstorm Matlab solvers in Python, compatible with MNE-Python.

• bycycle: Cycle-by-cycle analyses of neural oscillations.

• conpy: Functions and classes for performing connectivity analysis on MEG data.

• dcm2niix: DICOM to NIfTI converter

• dipy: Diffusion MRI Imaging in Python

• eeg_positions: Compute and plot standard EEG electrode positions.

• eeglabio: I/O support for EEGLAB files in Python

• eelbrain: Open-source Python toolkit for MEG and EEG data analysis.

• emd: Empirical Mode Decomposition in Python.

• fooof: fitting oscillations & one-over f

• fsleyes: FSLeyes is the FSL image viewer.

• meegkit: Denoising tools for M/EEG processing.

• meggie: User-friendly graphical user interface to do M/EEG analysis

• mffpy: Reader and Writer for Philips’ MFF file format.

• mne-ari: All-Resolutions Inference for M/EEG

• mne-bids: MNE-BIDS: Organizing MEG, EEG, and iEEG data according to the BIDS specification and facilitating their analysis with MNE-Python

• mne-bids-pipeline: A full-flegded processing pipeline for your MEG and EEG data

• mne-connectivity: mne-connectivity: A module for connectivity data analysis with MNE.

• mne-faster: Code for performing the FASTER pipeline on MNE-Python data structures.

• mne-features: MNE-Features software for extracting features from multivariate time series

• mne-gui-addons: MNE-Python GUI addons.

• mne-hcp: We provide Python tools for seamless integration of MEG data from the Human Connectome Project into the Python ecosystem

• mne-icalabel: MNE-ICALabel: Automatic labeling of ICA components from MEG, EEG and iEEG data with MNE.

• mne-kit-gui: A module for KIT MEG coregistration.

• mne-lsl: Real-time framework integrated with MNE-Python for online neuroscience research through LSL-compatible devices.

• mne-microstates: Code for microstate analysis, in combination with MNE-Python.

• mne-nirs: An MNE compatible package for processing near-infrared spectroscopy data

• mne-qt-browser: A new backend based on pyqtgraph for the 2D-Data-Browser in MNE-Python

• mne-rsa: Code for performing Representational Similarity Analysis on MNE-Python data structures.

• mnelab: A graphical user interface for MNE

• neurodsp: Digital signal processing for neural time series.

• neurokit2: The Python Toolbox for Neurophysiological Signal Processing.

• niseq: Group sequential tests for neuroimaging

• nitime: Nitime: timeseries analysis for neuroscience data

• openmeeg: Forward problems solver in the field of EEG and MEG.

• openneuro-py: A Python client for OpenNeuro.

• pactools: Estimation of phase-amplitude coupling (PAC) in neural time series, including with driven auto-regressive (DAR) models.

• posthoc: post-hoc modification of linear models

• pybv: pybv - a lightweight I/O utility for the BrainVision data format

• pycrostates: A simple open source Python package for EEG microstate segmentation.

• pyprep: A Python implementation of the preprocessing pipeline (PREP) for EEG data.

• pyriemann: Machine learning for multivariate data with Riemannian geometry

• neo: Neo is a package for representing electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats

• python-picard: Preconditoned ICA for Real Data

• sesameeg: Sequential Monte Carlo algorithm for multi dipolar source modeling in MEEG.

• sleepecg: A package for sleep stage classification using ECG data

• snirf: Interface and validator for SNIRF files

• tensorpac: Tensor-based Phase-Amplitude Coupling

• yasa: YASA: Analysis of polysomnography recordings.

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.