Highly Automated Dipole EStimation (HADES)
|Highly Automated Dipole EStimation (HADES)|
|Authors:||C. Campi, A. Pascarella, A. Sorrentino, M. Piana|
|Citation:||Computational intelligence and neuroscience 2011 : 982185. 2011|
|Web:||Bing Google Yahoo! — Google PDF|
|Article:||BASE Google Scholar PubMed|
|Restricted:||DTU Digital Library|
|Format:||BibTeX Template from PMID|
|Extract:||Talairach coordinates from linked PDF: CSV-formated wiki-formated|
Highly Automated Dipole EStimation (HADES) describes the HADES software.
Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This is due not only to the ill-posedness of the inverse problem but also to two intrinsic difficulties introduced by the dipolar model: the unknown number of sources and the nonlinear relationship between the source locations and the data. Recently, we have developed a new Bayesian approach, particle filtering, based on dynamical tracking of the dipole constellation. Contrary to many dipole-based methods, particle filtering does not assume stationarity of the source configuration: the number of dipoles and their positions are estimated and updated dynamically during the course of the MEG sequence. We have now developed a Matlab-based graphical user interface, which allows nonexpert users to do automatic dipole estimation from MEG data with particle filtering. In the present paper, we describe the main features of the software and show the analysis of both a synthetic data set and an experimental dataset.