Research Article | OPEN ACCESS
Blind Source Separation of fMRI Signals Using Joint Diagonalization Algorithm
1Amir A. Khaliq, 2I.M. Qureshi, 1Jawad A. Shah, 1Suheel A. Malik and 1Ihsanulhaq
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 2014 2:233-239
Received: March 18, 2013 | Accepted: April 04, 2013 | Published: January 10, 2014
Abstract
Blind Source Separation (BSS) is a model free source separation technique which decomposes observed mixture data into mixing matrix and source matrix both of which are unknown beforehand. One well known BSS algorithm is joint diagonalization which is from the algebraic class and in which mixing structures are recovered by jointly diagonalizing the source condition matrix. In this study we first review the existing joint diagonalizing algorithm and then propose a modified high order and exponential gradient of the algorithm. The proposed algorithm is tested on simulated images and synthetic fMRI signals. Quality and execution time of the extracted sources and time courses is compared with conventional JD algorithm.
Keywords:
Blind source separation, functional Magnet Resonance Imaging (fMRI), joint diagonalization,
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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.
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The authors have no competing interests.
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