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.. include:: links.inc
.. _getting_started:
Getting started
===============
.. _introduction_to_mne:
**MNE** is an academic software package that aims to provide data analysis
pipelines encompassing all phases of M/EEG data processing.
MNE started as tool written in C by Matti Hämäläinen while at MGH in Boston.
MNE was then extended with the Python programming language to implement
nearly all MNE-C’s functionality, offer transparent scripting, and
:ref:`extend MNE-C’s functionality considerably <what_can_you_do>`.
A basic :ref:`ch_matlab` is also available mostly
to allow reading and write MNE files. The sister :ref:`mne_cpp` project
aims to provide modular and open-source tools for acquisition,
visualization, and analysis.
.. note:: This package is based on the FIF file format from Neuromag. But, it
can read and convert CTF, BTI/4D, KIT and various EEG formats to
FIF (see :ref:`IO functions <ch_convert>`).
If you have been using MNE-C, there is no need to convert your fif
files to a new system or database -- MNE-Python works nicely with
the historical fif files.
Installation
------------
To get started with MNE, visit the installation instructions for
:ref:`MNE-Python <install_python_and_mne_python>` and
:ref:`MNE-C <install_mne_c>`:
.. container:: span box
.. raw:: html
<h3>MNE-Python</h3>
.. toctree::
:maxdepth: 2
install_mne_python
.. container:: span box
.. raw:: html
<h3>MNE-C</h3>
.. toctree::
:maxdepth: 2
install_mne_c
.. _what_can_you_do:
What can you do with MNE using Python?
--------------------------------------
- **Raw data visualization** to visualize recordings
(see :ref:`general_examples` for more).
- **Epoching**: Define epochs, baseline correction, handle conditions etc.
- **Averaging** to get Evoked data.
- **Compute SSP projectors** to remove ECG and EOG artifacts.
- **Compute ICA** to remove artifacts or select latent sources.
- **Maxwell filtering** to remove environmental noise.
- **Boundary Element Modeling**: single and three-layer BEM model
creation and solution computation.
- **Forward modeling**: BEM computation and mesh creation
(see :ref:`ch_forward`).
- **Linear inverse solvers** (dSPM, sLORETA, MNE, LCMV, DICS).
- **Sparse inverse solvers** (L1/L2 mixed norm MxNE, Gamma Map,
Time-Frequency MxNE, RAP-MUSIC).
- **Connectivity estimation** in sensor and source space.
- **Visualization of sensor and source space data**
- **Time-frequency** analysis with Morlet wavelets (induced power,
intertrial coherence, phase lock value) also in the source space.
- **Spectrum estimation** using multi-taper method.
- **Mixed Source Models** combining cortical and subcortical structures.
- **Dipole Fitting**
- **Decoding** multivariate pattern analysis of M/EEG topographies.
- **Compute contrasts** between conditions, between sensors, across
subjects etc.
- **Non-parametric statistics** in time, space and frequency
(including cluster-level).
- **Scripting** (batch and parallel computing)
Is that all you can do with MNE-Python?
---------------------------------------
Short answer is **No**! You can also do:
- detect heart beat QRS component
- detect eye blinks and EOG artifacts
- compute SSP projections to remove ECG or EOG artifacts
- compute Independent Component Analysis (ICA) to remove artifacts or
select latent sources
- estimate noise covariance matrix from Raw and Epochs
- visualize cross-trial response dynamics using epochs images
- compute forward solutions
- estimate power in the source space
- estimate connectivity in sensor and source space
- morph stc from one brain to another for group studies
- compute mass univariate statistics base on custom contrasts
- visualize source estimates
- export raw, epochs, and evoked data to other python data analysis
libraries e.g. pandas
- Raw movement compensation as you would do with Elekta Maxfilter™
- and many more things ...
What you're not supposed to do with MNE-Python
----------------------------------------------
- **Brain and head surface segmentation** for use with BEM
models -- use Freesurfer_.
|
