Advanced search
1 file | 691.71 KB
Author
Organization
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.
Keywords
Polynomial Chaos, EEG, Bayesian Inference, Random Partial Differential Equation (RPDE), sensitivity analysis, inverse problem

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 691.71 KB

Citation

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

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.
@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},
}