DocumentCode :
1543468
Title :
Stochastic Uncertainty Quantification of the Conductivity in EEG Source Analysis by Using Polynomial Chaos Decomposition
Author :
Gaignaire, Roman ; Crevecoeur, Guillaume ; Dupré, Luc ; Sabariego, Ruth V. ; Dular, Patrick ; Geuzaine, Christophe
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci. (ACE), Univ. of Liege, Liège, Belgium
Volume :
46
Issue :
8
fYear :
2010
Firstpage :
3457
Lastpage :
3460
Abstract :
The electroencephalogram (EEG) is one of the techniques used for the non-invasive diagnosis of patients suffering from epilepsy. EEG source localization identifies the neural activity, starting from measured EEG. This numerical localization procedure has a resolution, which is difficult to determine due to uncertainties in the EEG forward models. More specifically, the conductivities of the brain and the skull in the head models are not precisely known. In this paper, we propose the use of a non-intrusive stochastic method based on a polynomial chaos decomposition for quantifying the possible errors introduced by the uncertain conductivities of the head tissues. The accuracy and computational advantages of this non-intrusive method for EEG source analysis is illustrated. Further, the method is validated by means of Monte Carlo simulations.
Keywords :
Monte Carlo methods; bioelectric phenomena; biological tissues; brain models; chaos; electroencephalography; inverse problems; medical disorders; neurophysiology; patient diagnosis; stochastic processes; EEG forward model; EEG source analysis; Monte Carlo simulation; brain conductivity; electroencephalogram; epilepsy; head model; head tissue; inverse problems; neural activity; nonintrusive stochastic method; noninvasive diagnosis; numerical localization; polynomial chaos decomposition; skull; stochastic uncertainty quantification; Biomedical measurements; Brain modeling; Chaos; Conductivity; Electroencephalography; Head; Polynomials; Skull; Stochastic processes; Uncertainty; Inverse problems; non-intrusive methods; polynomial chaos decomposition; stochastic methods;
fLanguage :
English
Journal_Title :
Magnetics, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9464
Type :
jour
DOI :
10.1109/TMAG.2010.2044233
Filename :
5512970
Link To Document :
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