Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69496
Title: Bayesian radial basis function network for modeling fMRI data
Authors: Huaien, L.
Puthusserypady, S. 
Keywords: Bayesian lerning
fMRI
Nonlinear modeling
RBF network
Regularization
Issue Date: 2004
Source: Huaien, L.,Puthusserypady, S. (2004). Bayesian radial basis function network for modeling fMRI data. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings 26 I : 450-453. ScholarBank@NUS Repository.
Abstract: Noisy and nonlinear nature make fMRI signal processing a challenging problem. In this paper, we proposed and analyzed the Bayesian trained radial basis function (RBF) neural network in fMRI data processing. The method, which determines the regularisation 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.
Source Title: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/69496
ISSN: 05891019
Appears in Collections:Staff Publications

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