Sequential Monte Carlo algorithm for multi dipolar source modeling in MEEG.
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 . 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.
Cite our work
If you use this code in your project, please consider citing our work:
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