Abstract
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Article Information:
A Modified Infomax ICA Algorithm for fMRI Data Source Separation
Amir A. Khaliq, I.M. Qureshi, Suheel A. Malik and Jawad A. Shah
Corresponding Author: Muhammad Amir A Khaliq
Submitted: December 17, 2012
Accepted: January 23, 2013
Published: May 15, 2013 |
Abstract:
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This study presents a modified infomax model of Independent Component Analysis (ICA) for the source
separation problem of fMRI data. Functional MRI data is processed by different blind source separation techniques
including Independent Component Analysis (ICA). ICA is a statistical decomposition method used for multivariate
data source separation. ICA algorithm is based on independence of extracted sources for which different techniques
are used like kurtosis, negentropy, information maximization etc. The infomax method of ICA extracts unknown
sources from a number of mixtures by maximizing the negentropy thus ensuring independence. In this proposed
modified infomax model a higher order contrast function is used which results in fast convergence and accuracy.
The Proposed algorithm is applied to general simulated signals and simulated fMRI data. Comparison of correlation
results of the proposed algorithm with the conventional infomax algorithm shows better performance.
Key words: Blind source separation, functional Magnetic Resonance Imaging (fMRI), independent component analysis, medical image processing, , ,
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Abstract
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Cite this Reference:
Amir A. Khaliq, I.M. Qureshi, Suheel A. Malik and Jawad A. Shah, . A Modified Infomax ICA Algorithm for fMRI Data Source Separation. Research Journal of Applied Sciences, Engineering and Technology, (20): 4862-4868.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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