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Covariance based BSS Algorithm for Functional Magnetic Resonance Imaging (fMRI) data Source Separation

Author Affiliations

  • 1Department of Electronic Engineering, Faculty of Engineering and Technology International Islamic University Islamabad, PAKISTAN
  • 2 Department of Electronic Engineering, Air University, ISSS, Islamabad PAKISTAN

Res. J. Recent Sci., Volume 2, Issue (9), Pages 86-91, September,2 (2013)

Abstract

Functional Magnetic Resonance Imaging (fMRI) is a measuring technique used for brain functionality. Observed fMRI data is generally a mixture of hidden sources and corresponding time courses. Different blind source separation (BSS) techniques are used for extracting these hidden sources and time courses. In this work a differential covariance based blind source separation technique is proposed which relies on the difference of covariance of observed data and covariance of sources and mixing matrix. Performance of the proposed method is evaluated on the synthetic fMRI data. Finally comparison of ontained results is done with joint Diagonalization (JD) and Algorithm for multiple unknown source extraction (AMUSE). Comparision results shows that the proposed algoritham is better in terms of time and quality of extracted sources and time courses.

References

  1. Sharma Kalpa, Health IT in Indian Healthcare System: A New Initiative, Res. J. Recent Sci.,1(6), 83-86 (2012)
  2. Abdul Basit Shaikh, Muhammad Sarim, Sheikh Kashif Raffat, Mansoor Khan and Amin Chinoy, Bone Mineral Density Correlation against Bone Radiograph Texture Analysis: An Alternative Approach, Res. J. Recent Sci.,2(3), 87-91, March (2013)
  3. Shahaboddin Shamshirband and Ali Za'fari., Evaluation of the Performance of Intelligent Spray Networks Based On Fuzzy Logic, Res. J. Recent Sci., 1(8), 77-81 (2012)
  4. Bora Abhijit, Science Communication through Mass Media, Res. J. Recent Sci.,1(1), 10-15 (2012)
  5. Ogawa S., Lee T.M., Kay A.R. and Tank D.W., Brain magnetic resonance imaging with contrast dependent on blood oxygenation, in: Proceedings of the National Academy of Sciences of the United States of America., 87, 9868–9872 (1990)
  6. Friman O., Cadefamn J., Lundberg P., Borga M. and Knutsson H., Detection of neural activity in functional MRI using canonical correlation analysis, Magnetic Resonance in Medicine, 45(2), 323–330 (2001)
  7. Amir A., Khaliq I.M., Qureshi Jawad A Shah, Temporal Correlation based spatial filtering of Functional MRIs, CHIN. PHYS. LETT., 29(1),(2012)
  8. Thomas, Christopher G., Richard A. Harshman and Ravi S. Menon, Noise reduction in BOLD-based fMRI using component analysis, Neuroimage17 (3) 1521-1537 (2002)
  9. Hu D., Yan L., Liu Y., Zhou Z., Friston K.J., Tan C. and Wu D., Unified SPM–ICA for fMRI analysis, Neuroimage, 25(3), 746-755 (2005)
  10. Friston K.J., Ashburner J.T., Kiebel S.J., Nichols T.E. and Penny W.D. (Eds.), Statistical Parametric Mapping: The Analysis of Functional Brain Images: Academic Press (2011)
  11. Arfanakis K., Cordes D., Haughton V.M., Moritz C.H., Quigley M.A. and Meyerand M.E. Combining independent component analysis and correlation analysis to probe interregional connectivity in fMRI task activation datasets, Magnetic resonance imaging, 18(8), 921-930 (2000)
  12. Biswal, Bharat, F. Zerrin Yetkin, Victor M. Haughton, and James S. Hyde, Functional connectivity in the motor cortex of resting human brain using echoplanar mri, Magnetic resonance in medicine,34(4), 537-541 (1995)
  13. A. Hyv arinen., J. Karhunen., and E. Oja., Independent Component Analysis, New York: Wiley, (2001)
  14. Abdi, Hervé, and Lynne J. Williams., Principal component analysis, Wiley Interdisciplinary Reviews: Computational Statistics 2(4), 433-459 (2010)
  15. Lee, Daniel D., and H. Sebastian Seung., Learning the parts of objects by non-negative matrix factorization., Nature,401, ( 6755) , 788-791 (1999)
  16. .Ferdowsi, Saideh, Vahid Abolghasemi, and Saeid Sanei., A constrained NMF algorithm for BOLD detection in fMRI, In Machine Learning for Signal Processing (MLSP), IEEE International Workshop on, 77-82. IEEE (2010)
  17. Calhoun, Vince D., Jingyu Liu, and Tülay Adal., A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data., Neuroimage,45(1), Suppl S163.(2009)
  18. Bingham, Ella, and Aapo Hyvärinen., A fast fixed-point algorithm for independent component analysis of complex valued signals., International journal of neural systems,10, (01),1-8 (2000)
  19. Correa, Nicolle, Tülay Adal, and Vince D. Calhoun., Performance of blind source separation algorithms for fMRI analysis using a group ICA method., Magnetic resonance imaging,25(5), 684-694 (2007)
  20. Tong L., Soon V.C., Huang Y.F. and Liu R., AMUSE: a new blind identification algorithm. In Circuits and System., IEEE International Symposium on, 1784-1787 IEEE, (1990)