Bayesian radial basis function network for modeling fMRI data

Author

Huaien, Luo ; Puthusserypady, Sadasivan

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore

Volume

1

fYear

2004

fDate

1-5 Sept. 2004

Firstpage

450

Lastpage

453

Abstract

Noisy and nonlinear nature make fMRI signal processing a challenging problem. We proposed and analyzed the Bayesian trained radial basis function (RBF) neural network in fMRI data processing. The method, which determines the regularization parameter in RBF network automatically by Bayesian learning, is especially suitable for fMRI data processing. Both simulated and real fMRI data were tested. Results show that this approach could model fMRI signals and remove the slowly varying drift in the data sets as well.

Keywords

belief networks; biomedical MRI; learning (artificial intelligence); medical signal processing; physiological models; radial basis function networks; Bayesian learning; Bayesian radial basis function network; fMRI data modeling; fMRI data processing; regularization parameter; signal processing; Bayesian methods; Biological neural networks; Blood; Brain modeling; Data analysis; Feature extraction; Independent component analysis; Magnetic resonance imaging; Radial basis function networks; Signal processing; Bayesian lerning; Nonlinear modeling; RBF network; fMRI; regularization;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-8439-3

Type

conf

DOI

10.1109/IEMBS.2004.1403191

Filename

1403191