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Stochastic uncertainty quantification of the conductivity in EEG source analysis by using polynomial chaos decomposition

(2010) IEEE TRANSACTIONS ON MAGNETICS. 46(8). p.3457-3460
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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
DIPOLES, BOUNDS, BRAIN, HEAD MODELS, Inverse problems, non-intrusive methods, polynomial chaos decomposition, stochastic methods

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Please use this url to cite or link to this publication:

Chicago
Gaignaire, Roman, Guillaume Crevecoeur, Luc Dupré, Ruth V Sabariego, Patrick Dular, and Christophe Geuzaine. 2010. “Stochastic Uncertainty Quantification of the Conductivity in EEG Source Analysis by Using Polynomial Chaos Decomposition.” Ieee Transactions on Magnetics 46 (8): 3457–3460.
APA
Gaignaire, R., Crevecoeur, G., Dupré, L., Sabariego, R. V., Dular, P., & Geuzaine, C. (2010). Stochastic uncertainty quantification of the conductivity in EEG source analysis by using polynomial chaos decomposition. IEEE TRANSACTIONS ON MAGNETICS, 46(8), 3457–3460. Presented at the 17th International Conference on the Computation of Electromagnetic Fields (COMPUMAG 09).
Vancouver
1.
Gaignaire R, Crevecoeur G, Dupré L, Sabariego RV, Dular P, Geuzaine C. Stochastic uncertainty quantification of the conductivity in EEG source analysis by using polynomial chaos decomposition. IEEE TRANSACTIONS ON MAGNETICS. 2010;46(8):3457–60.
MLA
Gaignaire, Roman, Guillaume Crevecoeur, Luc Dupré, et al. “Stochastic Uncertainty Quantification of the Conductivity in EEG Source Analysis by Using Polynomial Chaos Decomposition.” IEEE TRANSACTIONS ON MAGNETICS 46.8 (2010): 3457–3460. Print.
@article{2116180,
  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.},
  author       = {Gaignaire, Roman and Crevecoeur, Guillaume and Dupr{\'e}, Luc and Sabariego, Ruth V and Dular, Patrick and Geuzaine, Christophe},
  issn         = {0018-9464},
  journal      = {IEEE TRANSACTIONS ON MAGNETICS},
  keyword      = {DIPOLES,BOUNDS,BRAIN,HEAD MODELS,Inverse problems,non-intrusive methods,polynomial chaos decomposition,stochastic methods},
  language     = {eng},
  location     = {Santa Catarina, Brazil},
  number       = {8},
  pages        = {3457--3460},
  title        = {Stochastic uncertainty quantification of the conductivity in EEG source analysis by using polynomial chaos decomposition},
  url          = {http://dx.doi.org/10.1109/TMAG.2010.2044233},
  volume       = {46},
  year         = {2010},
}

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