A fMRI data analysis method using a fast infomax-based ICA algorithm

Author

Yao, Dezhong ; Chen, Huafu ; Becker, Suzanna ; Zhou, Tiangang ; Zhuo, Yan ; Chen, Lin

Author_Institution

Dept. of Autom., Univ. of Sci. & Technol. of China, Chengdu, China

Volume

2

fYear

2001

fDate

2001

Firstpage

1105

Abstract

Independent component analysis (ICA) is a new technique in signal processing to extract statistically independent components from the observed multidimensional mixture of data. In this field, many algorithms have been proposed. An infomax-based fast algorithm for ICA is proposed, using information maximum likelihood estimation with the Newton iterative algorithm. The algorithm is second-order convergent. We specifically applied the algorithm to functional magnetic resonance imaging (fMRI) data, and the result is positive. These results lend validity to the proposed method as providing a reasonable physiological explanation for the fMRI data

Keywords

Newton method; biomedical MRI; data analysis; maximum likelihood estimation; medical image processing; statistical analysis; MLE; Newton iterative algorithm; fMRI data analysis method; functional magnetic resonance imaging; independent component analysis; infomax-based ICA algorithm; information maximum likelihood estimation; multidimensional data mixture; second-order convergent algorithm; signal processing; statistically independent components extraction; Automation; Blood; Convergence; Data analysis; Independent component analysis; Iterative algorithms; Magnetic resonance imaging; Maximum likelihood estimation; Multidimensional signal processing; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2001. Canadian Conference on

Conference_Location

Toronto, Ont.

ISSN

0840-7789

Print_ISBN

0-7803-6715-4

Type

conf

DOI

10.1109/CCECE.2001.933596

Filename

933596