NeuroCUDE fesete

Outline

NeuroCUDE is a cross-platform Python program for the automatic estimation of an unknown number of static multi-dipolar neural sources from MEG/EEG data. It can perform data analysis by means of two different Bayesian algorithms and can work either on a single topography (i.e. single time point, single frequency) or on a full time-series. NeuroCUDE comes with a user-friendly graphical interface together with its own visualization tool.

References

S. Sommariva and A. Sorrentino
Sequential Monte Carlo samplers for semi-linear inverse problems and application to Magnetoencephalography
Inverse Problems, 30 114020 (2014)
arXiv:1409.8109 [stat.AP]

A. Sorrentino, G. Luria and R. Aramini
Bayesian Multi-Dipole Modeling of a Single Topography in MEG by Adaptive Sequential Monte Carlo Samplers
Inverse Problems 30 045010 (2014)
arXiv:1305.4511v2 [stat.AP]

Download

Click here to download the code.
Documentation is almost ready and will be available soon: please check this page in the near future.
For further information or if you have any questions please do not hesitate to contact us at sommariva at dima.unige.it

System Requirements

NeuroCUDE works on Python 2.7 and is not compatible with Python 3. In addition to the standard Python packages (including NumPy and SciPy ), it requires the installation of PySide, Mayavi and, optionally, MNE-Python. The program has been tested on Windows 10 (x64), Mac OS X El Capitan 10.11.5 (x64) and Linux Ubuntu 15.10 (x64). Other versions/distributions of these operating systems may work too, but have not been tested.
Anaconda users need to downgrade SciPy package to version <0.16.0.

Terms of use

This is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.