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.. include:: ../links.inc
Before you install
==================
.. contents::
:local:
:depth: 1
Overview of the MNE tools suite
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- 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 <MNE-C manual_>`_.
- :doc:`MNE-Python <../python_reference>` reimplements the functionality of
MNE-C, and extends considerably the analysis and visualization capabilities.
MNE-Python is collaboratively developed and has more than 150 contributors.
- The :ref:`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`_.
- :doc:`MNE-CPP <../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`_)
What should I install?
^^^^^^^^^^^^^^^^^^^^^^
If you intend only to perform ERP, ERF, or other sensor-level analyses,
:doc:`MNE-Python <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 :doc:`MNE-C <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 most likely also need :doc:`FreeSurfer <freesurfer>` to
convert structural MRI scans into models of the scalp, inner/outer skull, and
cortical surfaces (specifically, for command-line functions
:ref:`gen_mne_flash_bem`, :ref:`gen_mne_watershed_bem`, and
:ref:`gen_mne_make_scalp_surfaces`).
Getting help
^^^^^^^^^^^^
Help with installation is available through the `MNE mailing list`_ and
`MNE gitter channel`_. See the :ref:`help` page for more information.
**Next:** :doc:`mne_python`
.. LINKS:
.. _MNELAB: https://github.com/cbrnr/mnelab
.. _autoreject: https://autoreject.github.io/
.. _alphaCSC: https://alphacsc.github.io/
.. _picard: https://pierreablin.github.io/picard/
.. _pactools: https://pactools.github.io/
.. _rsa: https://github.com/wmvanvliet/rsa
.. _microstate: https://github.com/wmvanvliet/mne_microstates
.. _conpy: https://aaltoimaginglanguage.github.io/conpy/
.. _eelbrain: https://eelbrain.readthedocs.io/en/stable/index.html
.. _posthoc: https://users.aalto.fi/~vanvlm1/posthoc/python/
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