Letters: A BFGS-ICA algorithm and application in localization of brain activities

Authors:
Huafu Chen;Dezhong Yao;Ling Zeng
Affiliations:
School of Life Science and Technology, School of Applied Math, University of Electronic Science and Technology of China, Chengdu 610054, PR China;School of Life Science and Technology, School of Applied Math, University of Electronic Science and Technology of China, Chengdu 610054, PR China;School of Life Science and Technology, School of Applied Math, University of Electronic Science and Technology of China, Chengdu 610054, PR China
Venue:
Neurocomputing
Year:
2005

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Abstract

The natural gradient and fixed-point algorithm are two of the most popular algorithms in independent component analysis (ICA). However, there still remain some problems to be solved in application to the processing of the functional magnetic resonance imaging (fMRI) data. Based on the BFGS quasi-Newton algorithm, this paper presents a novel BFGS-ICA algorithm framework in performing localization of brain activities with fMRI data. The new BFGS-ICA algorithm possesses properties of good convergence and immunity of initial point sensitivity. The convincing results of its application in fMRI show the potential of BFGS-ICA in detection of the brain activities.