SESAMEEG

SESAMEEG is a Python3 library providing the Bayesian multi-dipole localization method SESAME [1] (SEquential Semi-Analytic Montecarlo Estimator) for the automatic estimation of brain source currents from MEEG data, either in the time domain and in the frequency domain [2].

A mathematical description of the algorithm is available in the documentation.

Installation

To install this package, the easiest way is using pip. It will install both sesameeg 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.

Bug reports

Use the github issue tracker to report bugs.

Authors of the code

Gianvittorio Luria <luria@dima.unige.it>,

Sara Sommariva <sommariva@dima.unige.it>,

Alberto Sorrentino <sorrentino@dima.unige.it>.

Cite our work

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

[1]

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

[2]

7. Luria et al., Bayesian multi-dipole modelling in the frequency domain. J. Neurosci. Meth., 312 (2019) 27–36.