This is a Python3 implementation of the Bayesian multi-dipole modeling method and Sequential Monte Carlo algorithm SESAME described in1. 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 setup.py
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.
Use the github issue tracker to report bugs.
Sara Sommariva <sommariva@dima.unige.it>,
Alberto Sorrentino <sorrentino@dima.unige.it>.
If you use this code in your project, please consider citing our work:
- Sommariva and A. Sorrentino, Sequential Monte Carlo samplers for semi-linear inverse problems and application to Magnetoencephalography. Inverse Problems, 30 114020 (2014).