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2013 (Vol. 5, Issue: 20)
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 

Key words:  Blind source separation, functional Magnetic Resonance Imaging (fMRI), independent component analysis, medical image processing, , ,
Vol. 5 , (20): 4862-4868
Submitted Accepted Published
December 17, 2012 January 23, 2013 May 15, 2013
Abstract:

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.
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  Cite this Reference:
Amir A. Khaliq, I.M. Qureshi, Suheel A. Malik and Jawad A. Shah, 2013. A Modified Infomax ICA Algorithm for fMRI Data Source Separation.  Research Journal of Applied Sciences, Engineering and Technology, 5(20): 4862-4868.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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