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.. title:: MNE
.. include:: links.inc
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<a href="index.html"><img src="_static/mne_logo.png" border="0" alt="MNE" style="vertical-align: middle"></a>
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<a href="index.html"><img src="_static/institutions.png"" border="0" alt="Institutions"/></a>
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<br>
MNE is a community-driven software package designed for **processing
electroencephalography (EEG) and magnetoencephalography (MEG) data**
providing comprehensive tools and workflows for
(:ref:`among other things <what_can_you_do>`):
1. Preprocessing and denoising
2. Source estimation
3. Time–frequency analysis
4. Statistical testing
5. Estimation of functional connectivity
6. Applying machine learning algorithms
7. Visualization of sensor- and source-space data
MNE includes a comprehensive Python_ package supplemented by tools compiled
from C code for the LINUX and Mac OSX operating systems, as well as a MATLAB toolbox.
**From raw data to source estimates in about 30 lines of code** (Try it :ref:`by installing it <getting_started>` or `in an experimental online demo <http://mybinder.org/repo/mne-tools/mne-binder/notebooks/plot_introduction.ipynb>`_!):
.. code:: python
>>> import mne # doctest: +SKIP
>>> raw = mne.io.read_raw_fif('raw.fif', preload=True) # load data # doctest: +SKIP
>>> raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels # doctest: +SKIP
>>> raw.filter(l_freq=None, h_freq=40.0) # low-pass filter data # doctest: +SKIP
>>> # Extract epochs and save them:
>>> picks = mne.pick_types(raw.info, meg=True, eeg=True, eog=True, # doctest: +SKIP
>>> exclude='bads') # doctest: +SKIP
>>> events = mne.find_events(raw) # doctest: +SKIP
>>> reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6) # doctest: +SKIP
>>> epochs = mne.Epochs(raw, events, event_id=1, tmin=-0.2, tmax=0.5, # doctest: +SKIP
>>> proj=True, picks=picks, baseline=(None, 0), # doctest: +SKIP
>>> preload=True, reject=reject) # doctest: +SKIP
>>> # Compute evoked response and noise covariance
>>> evoked = epochs.average() # doctest: +SKIP
>>> cov = mne.compute_covariance(epochs, tmax=0) # doctest: +SKIP
>>> evoked.plot() # plot evoked # doctest: +SKIP
>>> # Compute inverse operator:
>>> fwd_fname = 'sample_audvis−meg−eeg−oct−6−fwd.fif' # doctest: +SKIP
>>> fwd = mne.read_forward_solution(fwd_fname, surf_ori=True) # doctest: +SKIP
>>> inv = mne.minimum_norm.make_inverse_operator(raw.info, fwd, # doctest: +SKIP
>>> cov, loose=0.2) # doctest: +SKIP
>>> # Compute inverse solution:
>>> stc = mne.minimum_norm.apply_inverse(evoked, inv, lambda2=1./9., # doctest: +SKIP
>>> method='dSPM') # doctest: +SKIP
>>> # Morph it to average brain for group study and plot it
>>> stc_avg = mne.morph_data('sample', 'fsaverage', stc, 5, smooth=5) # doctest: +SKIP
>>> stc_avg.plot() # doctest: +SKIP
MNE development is driven by :ref:`extensive contributions from the community <whats_new>`.
Direct financial support for the project has been provided by:
- (US) National Institute of Biomedical Imaging and Bioengineering (NIBIB)
grants 5R01EB009048 and P41EB015896 (Center for Functional Neuroimaging
Technologies)
- (US) NSF awards 0958669 and 1042134.
- (US) NCRR *Center for Functional Neuroimaging Technologies* P41RR14075-06
- (US) NIH grants 1R01EB009048-01, R01 EB006385-A101, 1R01 HD40712-A1, 1R01
NS44319-01, and 2R01 NS37462-05
- (US) Department of Energy Award Number DE-FG02-99ER62764 to The MIND
Institute.
- (FR) IDEX Paris-Saclay, ANR-11-IDEX-0003-02, via the
`Center for Data Science <http://www.datascience-paris-saclay.fr/>`_.
- (FR) European Research Council (ERC) Starting Grant (ERC-YStG-263584).
- (FR) French National Research Agency (ANR-14-NEUC-0002-01).
- (FR) European Research Council (ERC) Starting Grant (ERC-YStG-676943).
- Amazon Web Services - Research Grant issued to Denis A. Engemann
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<h2>Documentation</h2>
.. toctree::
:maxdepth: 1
getting_started
tutorials
auto_examples/index
faq
contributing
.. toctree::
:maxdepth: 1
python_reference
manual/index
whats_new
.. toctree::
:maxdepth: 1
cite
references
cited
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<h2>Community</h2>
* Analysis talk: join the `MNE mailing list`_
* `Feature requests and bug reports on GitHub <https://github.com/mne-tools/mne-python/issues/>`_
* `Chat with developers on Gitter <https://gitter.im/mne-tools/mne-python>`_
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<h2>Versions</h2>
<ul>
<li><a href=http://martinos.org/mne/stable>Stable</a></li>
<li><a href=http://martinos.org/mne/dev>Development</a></li>
</ul>
<div style="float: left; padding: 10px; width: 100%;">
<a class="twitter-timeline" href="https://twitter.com/mne_python" data-widget-id="317730454184804352">Tweets by @mne_python</a>
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