Bayesian inference in the uncertain EEG problem including local information and a sensor correlation matrix

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:252

Article

De Staelen, R. H.¹  Crevecoeur, G.²  Goessens, T.¹  Slodicka, M.¹ 

[1] Univ Ghent, Res Grp Numer Anal & Math Modeling NaM2, Dept Math Anal, B-9000 Ghent, Belgium

[2] Univ Ghent, Dept Elect Energy Syst & Automat EESA, B-9000 Ghent, Belgium

关键词: Inverse problem;    Bayesian inference;    Polynomial Chaos;    Conductivity;    Correlation;    EEG;   

DOI  :  10.1016/j.cam.2012.12.016

来源: Elsevier

【 摘 要 】

We present a framework based on Bayesian inference to combine expert judgment and the problem of an uncertain conductivity in the electroencephalography (EEG) inverse problem. A three layer spherical head model with different and random layer conductivities is considered. The randomness is modeled by Legendre Polynomial Chaos. Using this Polynomial Chaos we build on previous work to obtain a correlation matrix for the error used in the likelihood function of the Bayesian procedure. We compare with a classical isotropic correlation. (c) 2012 Elsevier B.V. All rights reserved.

【 授权许可】

Free