Please use this identifier to cite or link to this item: https://doi.org/10.1142/S0129065706000561
Title: Spatio-temporal modeling and analysis of fMRI data using NARX neural network
Authors: Luo, H.
Puthusserypady, S. 
Keywords: Bayesian learning
fMRI
NARX
RBF
Spatio-temporal modelling
Issue Date: Apr-2006
Source: Luo, H., Puthusserypady, S. (2006-04). Spatio-temporal modeling and analysis of fMRI data using NARX neural network. International Journal of Neural Systems 16 (2) : 139-149. ScholarBank@NUS Repository. https://doi.org/10.1142/S0129065706000561
Abstract: This paper presents spatio-temporal modeling and analysis methods to fMRl data. Based on the nonlinear autoregressive with exogenous inputs (NARX) model realized by the Bayesian radial basis function (RBF) neural networks, two methods (NARX-1 and NARX-2) are proposed to capture the unknown complex dynamics of the brain activities. Simulation results on both synthetic and real fMRI data, clearly show that the proposed schemes outperform the conventional t-test method in detecting the activated regions of the brain. © World Scientific Publishing Company.
Source Title: International Journal of Neural Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/57462
ISSN: 01290657
DOI: 10.1142/S0129065706000561
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