MNE-Connectivity is an open-source Python package for connectivity and related measures of MEG, EEG, or iEEG data built on top of the MNE-Python API. It includes modules for data input/output, visualization, common connectivity analysis, and post-hoc statistics and processing.
This project was initially ported over from mne-python starting v0.23, by Adam Li as part of Google Summer of Code 2021. Subsequently v0.1 and v0.2 releases were done as part of GSoC period. Future development will occur in subsequent versions.
Stable MNE-Connectivity documentation is available online.
To install the latest stable version of MNE-Connectivity, you can use pip in a terminal:
pip install -U mne-connectivityFor more complete instructions and more advanced installation methods (e.g. for the latest development version), see the installation guide.
To install the latest version of the code using pip open a terminal and type:
pip install -U https://github.com/mne-tools/mne-connectivity/archive/main.zipTo get the latest code using git, open a terminal and type:
git clone git://github.com/mne-tools/mne-connectivity.gitAlternatively, you can also download a zip file of the latest development version.
Please see the documentation on the MNE-Connectivity homepage:
https://github.com/mne-tools/mne-connectivity/blob/main/CONTRIBUTING.md
In the neuroscience community as of 2021, the term "functional connectivity" can have many different meanings and comprises many different measures. Some of these measures are directed (i.e. try to map a statistical causal relationship between brain regions), others are non-directed. Please note that the interpretation of your functional connectivity measure depends on the data and underlying assumptions. For a taxonomy of functional connectivity measures and information on the interpretation of those measures, we refer to Bastos and Schoffelen.
In mne-connectivity, we do not claim that any of our measures imply causal connectivity.