Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71099
Title: NARX neural networks for dynamical modelling of fMRI data
Authors: Luo, H.
Puthusserypady, S. 
Issue Date: 2006
Source: Luo, H.,Puthusserypady, S. (2006). NARX neural networks for dynamical modelling of fMRI data. IEEE International Conference on Neural Networks - Conference Proceedings : 542-546. ScholarBank@NUS Repository.
Abstract: Functional Magnetic Resonance Imaging (fMRI) is an important technique to study the human brain (the most complex biological dynamical system) functions which are often described by the hemodynamic responses (HDR). It measures the changes of the blood oxygenation level dependent (BOLD) signals due to the neural activities. The measured fMRI data is the response of the human brain to a particular processing task. In this paper, the nonlinear autoregressive with exogenous inputs (NARX) neural networks are investigated as a method to model the dynamics underlying the fMRI data. Studies on both simulated as well as real event-related fMRI data show that the proposed scheme can capture the underlying dynamics of the brain and reconstruct the BOLD signals from the measured noisy fMRI data. In addition, a good estimate of the HDR of the brain is also obtained. © 2006 IEEE.
Source Title: IEEE International Conference on Neural Networks - Conference Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/71099
ISBN: 0780394909
ISSN: 10987576
Appears in Collections:Staff Publications

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