Research Article | OPEN ACCESS
A Modified Infomax ICA Algorithm for fMRI Data Source Separation
1Amir A. Khaliq, 2I.M. Qureshi, 1Suheel A Malik and 1Jawad A. Shah
1Department of Electronic Engineering, International Islamic University Islamabad, Pakistan
2Department of Electronic Engineering Air University, ISSS Islamabad, Pakistan
Research Journal of Applied Sciences, Engineering and Technology 2013 20:4862-4868
Received: December 17, 2012 | Accepted: January 23, 2013 | Published: 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.
Keywords:
Blind source separation, functional Magnetic Resonance Imaging (fMRI), independent component analysis, medical image processing,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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