Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCSI.2005.857083
Title: A sparse Bayesian method for determination of flexible design matrix for fMRI data analysis
Authors: Luo, H.
Puthusserypady, S. 
Keywords: Design matrix
Functional magnetic resonance imaging (fMRI)
Generalized linear model
Receiver operating characteristic (ROC) curve
Sparse Bayesian learning
Issue Date: Dec-2005
Source: Luo, H., Puthusserypady, S. (2005-12). A sparse Bayesian method for determination of flexible design matrix for fMRI data analysis. IEEE Transactions on Circuits and Systems I: Regular Papers 52 (12) : 2699-2706. ScholarBank@NUS Repository. https://doi.org/10.1109/TCSI.2005.857083
Abstract: The construction of a design matrix is critical to the accurate detection of activation regions of the brain in functional magnetic resonance imaging (fMRI). The design matrix should be fiexible to capture the unknown slowly varying drifts as well as robust enough to avoid overfitting. In this paper, a sparse Bayesian learning method is proposed to determine a suitable design matrix for fMRI data analysis. Based on a generalized linear model, this learning method lets the data itself determine the form of the regressors in the design matrix. It automatically finds those regressors that are relevant to the generation of the fMRI data and discards the others that are irrelevant. The proposed approach integrates the advantages of currently employed methods of fMRI data analysis (the model-driven and the data-driven methods). Results from the simulation studies clearly reveal the superiority of the proposed scheme to the conventional t-test method of fMRI data analysis. © 2005 IEEE.
Source Title: IEEE Transactions on Circuits and Systems I: Regular Papers
URI: http://scholarbank.nus.edu.sg/handle/10635/54820
ISSN: 10577122
DOI: 10.1109/TCSI.2005.857083
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