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Polynomial chaos and Bayesian inference in RPDE's: a biomedical application

Rob De Staelen UGent, Karim Beddek and Tineke Goessens (2011) Proceedings of the 11th international conference on computational and mathematical methods in science and engineering, CMMSE 2011.
abstract
The electroencephalograph (EEG) is one of the most influential tools in the diagnosis of epilepsy and seizures. It measures electrical discharges of neurons in the human brain. The latter consists of many regions, all with a different electrical conductivity. Unfortunately one cannot measure this non invasively, e.g. preoperatively. In this paper, we investigate the uncertainty induced on the location of EEG current dipoles. A Bayesian framework is used, so as to include modeling error and noise, but combined with Polynomial Chaos expansions to represent random variables, speeding up computations. We evaluate this technique on a spherical head model with a standard clinical 27 sensor positioning.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
Polynomial Chaos, EEG, Bayesian Inference, Random Partial Differential Equation (RPDE), sensitivity analysis, inverse problem
in
Proceedings of the 11th international conference on computational and mathematical methods in science and engineering, CMMSE 2011
pages
11 pages
conference name
11th International conference on Computational and Mathematical Methods in Science and Engineering (CMMSE 2011)
conference location
Alicante, Spain
conference start
2011-06-26
conference end
2011-06-30
ISBN
9788461461677
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1219352
handle
http://hdl.handle.net/1854/LU-1219352
date created
2011-05-08 17:30:40
date last changed
2016-12-19 15:35:09
@inproceedings{1219352,
  abstract     = {The electroencephalograph (EEG) is one of the most influential tools in the diagnosis of epilepsy and seizures. It measures electrical discharges of neurons in the human brain. The latter consists of many regions, all with a different electrical conductivity. Unfortunately one cannot measure this non invasively, e.g. preoperatively.  In this paper, we investigate the uncertainty induced on the location of EEG current dipoles. A Bayesian framework is used, so as to include modeling error and noise, but combined with Polynomial Chaos expansions to represent random variables, speeding up computations. We evaluate this technique on a spherical head model with a standard clinical 27 sensor positioning.},
  author       = {De Staelen, Rob and Beddek, Karim and Goessens, Tineke},
  booktitle    = {Proceedings of the 11th international conference on computational and mathematical methods in science and engineering, CMMSE 2011},
  isbn         = {9788461461677},
  keyword      = {Polynomial Chaos,EEG,Bayesian Inference,Random Partial Differential Equation (RPDE),sensitivity analysis,inverse problem},
  language     = {eng},
  location     = {Alicante, Spain},
  pages        = {11},
  title        = {Polynomial chaos and Bayesian inference in RPDE's: a biomedical application},
  year         = {2011},
}

Chicago
De Staelen, Rob, Karim Beddek, and Tineke Goessens. 2011. “Polynomial Chaos and Bayesian Inference in RPDE’s: a Biomedical Application.” In Proceedings of the 11th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2011.
APA
De Staelen, Rob, Beddek, K., & Goessens, T. (2011). Polynomial chaos and Bayesian inference in RPDE’s: a biomedical application. Proceedings of the 11th international conference on computational and mathematical methods in science and engineering, CMMSE 2011. Presented at the 11th International conference on Computational and Mathematical Methods in Science and Engineering (CMMSE 2011).
Vancouver
1.
De Staelen R, Beddek K, Goessens T. Polynomial chaos and Bayesian inference in RPDE’s: a biomedical application. Proceedings of the 11th international conference on computational and mathematical methods in science and engineering, CMMSE 2011. 2011.
MLA
De Staelen, Rob, Karim Beddek, and Tineke Goessens. “Polynomial Chaos and Bayesian Inference in RPDE’s: a Biomedical Application.” Proceedings of the 11th International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2011. 2011. Print.