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Sequential Monte Carlo algorithm for multi dipolar source modeling in MEEG.

Project description

SESAMEEG: SEquential Semi-Analytic Montecarlo Estimation for MEEG

This is a Python3 implementation of the Bayesian multi-dipole modeling method and Sequential Monte Carlo algorithm SESAME described in [1]. The algorithm takes in input a forward solution and a MEEG evoked data time series, and outputs a posterior probability map for brain activity, as well as estimates of the number of sources, their locations and their amplitudes.


To install this package, the easiest way is using pip. It will install this package and its dependencies. The depends on numpy, scipy and mne for the installation so it is advised to install them beforehand. To install this package, please run the following commands:

(Latest stable version)

pip install numpy scipy mne
pip install sesameeg

If you do not have admin privileges on the computer, use the --user flag with pip. To upgrade, use the --upgrade flag provided by pip.

To check if everything worked fine, you can run:

python -c 'import sesameeg'

and it should not give any error messages.

Bug reports

Use the github issue tracker to report bugs.

Authors of the code

Gianvittorio Luria <>,
Sara Sommariva <>,
Alberto Sorrentino <>.

Cite our work

If you use this code in your project, please consider citing our work:

  1. Sommariva and A. Sorrentino, Sequential Monte Carlo samplers for semi-linear inverse problems and application to Magnetoencephalography. Inverse Problems, 30 114020 (2014).

Project details

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