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Highly Automated Dipole EStimation (HADES)
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...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3061326/ https://www.ncbi.nlm.nih.gov/pubmed/21437232 http://dx.doi.org/10.1155/2011/982185 |
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author | Campi, C. Pascarella, A. Sorrentino, A. Piana, M. |
author_facet | Campi, C. Pascarella, A. Sorrentino, A. Piana, M. |
author_sort | Campi, C. |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-3061326 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-30613262011-03-24 Highly Automated Dipole EStimation (HADES) Campi, C. Pascarella, A. Sorrentino, A. Piana, M. Comput Intell Neurosci Research Article 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. Hindawi Publishing Corporation 2011 2011-03-06 /pmc/articles/PMC3061326/ /pubmed/21437232 http://dx.doi.org/10.1155/2011/982185 Text en Copyright © 2011 C. Campi et al. https://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Research Article Campi, C. Pascarella, A. Sorrentino, A. Piana, M. Highly Automated Dipole EStimation (HADES) |
title | Highly Automated Dipole EStimation (HADES) |
title_full | Highly Automated Dipole EStimation (HADES) |
title_fullStr | Highly Automated Dipole EStimation (HADES) |
title_full_unstemmed | Highly Automated Dipole EStimation (HADES) |
title_short | Highly Automated Dipole EStimation (HADES) |
title_sort | highly automated dipole estimation (hades) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3061326/ https://www.ncbi.nlm.nih.gov/pubmed/21437232 http://dx.doi.org/10.1155/2011/982185 |
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