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Computational Intelligence and Neuroscience
Volume 2011, Article ID 982185, 11 pages
http://dx.doi.org/10.1155/2011/982185
Research Article

Highly Automated Dipole EStimation (HADES)

1Dipartimento di Matematica, Università di Genova, 16126 Genova, Italy
2Sezione di Fisiologia, Dipartimento di Neuroscienze, Università di Parma, 143121 Parma, Italy

Received 29 July 2010; Revised 5 November 2010; Accepted 17 January 2011

Academic Editor: Sylvain Baillet

Copyright © 2011 C. Campi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.