Mixed-norms and Hierarchical Bayesian models for the M/EEG inverse problem
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README.rst

Mixed-norms and HBMs for the M/EEG inverse problem

Travis

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 [1] 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.

Dependencies

  • mne
  • numba

For instructions on how to install MNE see: http://martinos.org/mne/stable/install_mne_python.html

Installation

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

Cite

If you use this code in your project, please cite:

[1] 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.