This package is designed for sensor- and source-space analysis of [M/E]EG data, including frequency-domain and time-frequency analyses, MVPA/decoding and non-parametric statistics. This package generally evolves quickly and user contributions can easily be incorporated thanks to the open development environment .
Get more information
If you're unfamiliar with MNE or MNE-Python, you can visit the MNE homepage for full user documentation.
Get the latest code
To get the latest code using git, simply type:
$ git clone git://github.com/mne-tools/mne-python.git
If you don't have git installed, you can download a zip of the latest code.
As with most Python packages, to install the latest stable version of MNE-Python, you can do:
$ pip install mne
For more complete instructions and more advanced install methods (e.g. for the latest development version), see the getting started page page.
The minimum required dependencies to run the software are:
- Python >= 2.7
- NumPy >= 1.8
- SciPy >= 0.12
- matplotlib >= 1.3
For full functionality, some functions require:
- scikit-learn >= 0.18
- nibabel >= 2.1.0
- pandas >= 0.12
To use NVIDIA CUDA for resampling and FFT FIR filtering, you will also need to install the NVIDIA CUDA SDK, pycuda, and scikits.cuda. See the getting started page for more information.
Contribute to mne-python
Please see the documentation on the mne-python homepage:
MNE-Python is BSD-licenced (3 clause):
This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative.
Copyright (c) 2011, authors of MNE-Python All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- Neither the names of MNE-Python authors nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.
This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the copyright owner or contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.