hackathon_mne_mvc

Multivariate connectivity methods implemented in Python and based on functions and objects available in MNE.

The methods include:

• Maximised imaginary coherence [1]
• Multivariate interaction measure [1]
• Granger causality based on state space models [2 & 3] with optional time-reversal [4]

Requirements

1. The base Anaconda package.
2. The MNE and MNE-connectivity packages.

Use

Use the example_pipeline.py script to generate the different multivariate results based on some example data (Data/epochs-epo.fif) using the specified settings (Settings/pipeline_settings.json).

References

[1] Ewald et al. (2012). NeuroImage. DOI: 10.1016/j.neuroimage.2011.11.084.

[2] Barnett & Seth (2014). Journal of Neuroscience Methods. DOI: 10.1016/j.jneumeth.2013.10.018.

[3] Barnett & Seth (2015). Physical Review E. DOI: 10.1103/PhysRevE.91.040101.

[4] Winkler et al. (2016). IEEE Transactions on Signal Processing. DOI: 10.1109/TSP.2016.2531628.