Mixed-norms and HBMs for the M/EEG inverse problem
This repository hosts the code to solve the M/EEG inverse problem. It improves the majorization-minimization (MM) techniques by probing the multimodal posterior density using Markov Chain Monte-Carlo (MCMC) techniques applied to Hierarchical Bayesian models (HBM).
More details in  to see how this method reveals the different modes of the posterior distribution in order to explore and quantify the inherent uncertainty and ambiguity of such ill-posed inference procedure. In the context of M/EEG, each mode corresponds to a plausible configuration of neural sources, which is crucial for data interpretation, especially in clinical contexts.
For instructions on how to install MNE see: http://martinos.org/mne/stable/install_mne_python.html
To install the package, the simplest way is to use pip to get the latest version of the code:
$ pip install git+https://github.com/agramfort/bayes_mxne.git#egg=bayes_mxne
If you use this code in your project, please cite:
 Bekhti, Y., Lucka, F., Salmon, J., & Gramfort, A. (2018). A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging. Inverse Problems, Volume 34, Number 8.
Get the PDF of the paper.