MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more.
Documentation for MNE-Python encompasses installation instructions, tutorials, and examples for a wide variety of topics, contributing guidelines, and an API reference.
The user forum is the best place to ask questions about MNE-Python usage or the contribution process. The forum also features job opportunities and other announcements.
If you find a bug or have an idea for a new feature that should be added to MNE-Python, please use the issue tracker of our GitHub repository.
To install the latest stable version of MNE-Python with minimal dependencies only, use pip in a terminal:
$ pip install --upgrade mneThe current MNE-Python release requires Python 3.8 or higher. MNE-Python 0.17 was the last release to support Python 2.7.
For more complete instructions, including our standalone installers and more advanced installation methods, please refer to the installation guide.
To install the latest development version of MNE-Python using pip, open a terminal and type:
$ pip install --upgrade git+https://github.com/mne-tools/mne-python@mainTo clone the repository with git, open a terminal and type:
$ git clone https://github.com/mne-tools/mne-python.gitThe minimum required dependencies to run MNE-Python are:
- Python ≥ 3.8
- NumPy ≥ 1.21.2
- SciPy ≥ 1.7.1
- Matplotlib ≥ 3.5.0
- Pooch ≥ 1.5
- tqdm
- Jinja2
- decorator
- lazy_loader
For full functionality, some functions require:
- scikit-learn ≥ 1.0
- Joblib ≥ 0.15 (for parallelization)
- mne-qt-browser ≥ 0.1 (for fast raw data visualization)
- Qt ≥ 5.12 via one of the following bindings (for fast raw data visualization and interactive 3D visualization):
- Numba ≥ 0.54.0
- NiBabel ≥ 3.2.1
- OpenMEEG ≥ 2.5.6
- pandas ≥ 1.3.2
- Picard ≥ 0.3
- CuPy ≥ 9.0.0 (for NVIDIA CUDA acceleration)
- DIPY ≥ 1.4.0
- imageio ≥ 2.8.0
- PyVista ≥ 0.32 (for 3D visualization)
- PyVistaQt ≥ 0.4 (for 3D visualization)
- mffpy ≥ 0.5.7
- h5py
- h5io
- pymatreader
Please see the contributing guidelines on our documentation website.
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MNE-Python is BSD-licensed (BSD-3-Clause):
This software is OSI Certified Open Source Software. OSI Certified is a certification mark of the Open Source Initiative.
Copyright (c) 2011-2022, 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.