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Journal of Applied Mathematics
Volume 2012, Article ID 528469, 19 pages
http://dx.doi.org/10.1155/2012/528469
Research Article

Scanning Reduction Strategy in MEG/EEG Beamformer Source Imaging

School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju 500-712, Republic of Korea

Received 20 August 2011; Accepted 18 September 2011

Academic Editor: Venky Krishnan

Copyright © 2012 Jun Hee Hong and Sung Chan Jun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Hämäläinen, R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa, “Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain,” Reviews of Modern Physics, vol. 65, no. 2, pp. 413–497, 1993. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Baillet, J. C. Mosher, and R. M. Leahy, “Electromagnetic brain mapping,” IEEE Signal Processing Magazine, vol. 18, no. 6, pp. 14–30, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. B. D. Van Veen and K. M. Buckley, “Beamforming: a versatile approach to spatial filtering,” IEEE ASSP Magazine, vol. 5, no. 2, pp. 4–24, 1988. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Sekihara, S. S. Nagarajan, D. Poeppel, and A. Marantz, “Performance of an MEG adaptive-beamformer technique in the presence of correlated neural activities: effects on signal intensity and time-course estimates,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 12, pp. 1534–1546, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. B. D. Van Veen, W. Van Drongelen, M. Yuchtman, and A. Suzuki, “Localization of brain electrical activity via linearly constrained minimum variance spatial filtering,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 9, pp. 867–880, 1997. View at Publisher · View at Google Scholar · View at Scopus
  6. M. J. Brookes, C. M. Stevenson, G. R. Barnes et al., “Beamformer reconstruction of correlated sources using a modified source model,” NeuroImage, vol. 34, no. 4, pp. 1454–1465, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. M. X. Huang, J. J. Shih, R. R. Lee et al., “Commonalities and differences among vectorized beamformers in electromagnetic source imaging,” Brain Topography, vol. 16, no. 3, pp. 139–158, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. K. Nazarpour, Y. Wongsawat, S. Sanei, S. Oraintara, and J. A. Chambers, “A robust minimum variance beamforming approach for the removal of the eye-blink artifacts from EEGs,” in Proceedings of the 29th Annual International Conference of IEEE, Engineering in Medicine and Biology Society (EMBS '07), pp. 6211–6214, Lyon, France, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Nazarpour, Y. Wongsawat, S. Sanei, J. A. Chambers, and S. Oraintara, “Removal of the eye-blink artifacts from EEGs via STF-TS modeling and robust minimum variance beamforming,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 9, pp. 2221–2231, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Sekihara and S. S. Nagarajan, Adaptive Spatial Filters for Electromagnetic Brain Imaging, Springer, Berlin, Germany, 2008.
  11. K. Sekihara, M. Sahani, and S. S. Nagarajan, “Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction,” NeuroImage, vol. 25, no. 4, pp. 1056–1067, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Gross and A. A. Ioannides, “Linear transformations of data space in MEG,” Physics in Medicine and Biology, vol. 44, no. 8, pp. 2081–2097, 1999. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Hillebrand, K. D. Singh, I. E. Holliday, P. L. Furlong, and G. R. Barnes, “A new approach to neuroimaging with magnetoencephalography,” Human Brain Mapping, vol. 25, no. 2, pp. 199–211, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Robinson and J. Vrba, “Functional neuroimaging by synthetic aperture magnetometry (SAM),” in Recent Advances in Biomagnetism, T. Yoshimoto, M. Kotani, S. Kuriki, H. Karibe, and N. Nakasato, Eds., pp. 302–305, Tohoku University Press, Sendai, Japan, 1999. View at Google Scholar
  15. K. Sekihara, S. S. Nagarajan, D. Poeppel, A. Marantz, and Y. Miyashita, “Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources,” Human Brain Mapping, vol. 15, no. 4, pp. 199–215, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. M. E. Spencer, R. Leahy, J. Mosher, and P. Lewis, “Adaptive filters for monitoring localized brain activity from surface potential time series,” in Proceedings of the IEEE 26th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 156–161, Pacific Grove, Calif, USA, October 1992. View at Publisher · View at Google Scholar
  17. O. Steinsträter, S. Sillekens, M. Junghoefer, M. Burger, and C. H. Wolters, “Sensitivity of beamformer source analysis to deficiencies in forward modeling,” Human Brain Mapping, vol. 31, no. 12, pp. 1907–1927, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Besserve, K. Jerbi, F. Laurent, S. Baillet, J. Martinerie, and L. Garnero, “Classification methods for ongoing EEG and MEG signals,” Biological Research, vol. 40, no. 4, pp. 415–437, 2008. View at Google Scholar · View at Scopus
  19. P. Poolman, R. M. Frank, P. Luu, S. M. Pederson, and D. M. Tucker, “A single-trial analytic framework for EEG analysis and its application to target detection and classification,” NeuroImage, vol. 42, no. 2, pp. 787–798, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Ahn, J. H. Hong, and S. C. Jun, “Source space based brain computer interface,” in Proceedings of the 17th International Conference on Biomagnetism Advances in Biomagnetism (Biomag '10), vol. 28, part 12, pp. 366–369, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K. R. Müller, “Optimizing spatial filters for robust EEG single-trial analysis,” IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 41–56, 2008. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Congedo, F. Lotte, and A. Lécuyer, “Classification of movement intention by spatially filtered electromagnetic inverse solutions,” Physics in Medicine and Biology, vol. 51, no. 8, pp. 1971–1989, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Kamousi, Z. Liu, and B. He, “Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 2, pp. 166–171, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Yuan, A. Doud, A. Gururajan, and B. He, “Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, no. 5, pp. 425–431, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. J. H. Hong and S. C. Jun, “Speeding-up MEG beamforming source imaging by correlation between measurement and lead-field vector,” in Proceedings of the 17th International Conference on Biomagnetism Advances in Biomagnetism (BIOMAG '10), vol. 28, part 5, pp. 148–151, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. K. Sekihara, S. S. Nagarajan, D. Poeppel, A. Marantz, and Y. Miyashita, “Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 7, pp. 760–771, 2001. View at Publisher · View at Google Scholar · View at Scopus
  27. K. Sekihara, S. S. Nagarajan, D. Poeppel, and A. Marantz, “Performance of an MEG adaptive-beamformer source reconstruction technique in the presence of additive low-rank interference,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 1, pp. 90–99, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. J. Sarvas, “Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem,” Physics in Medicine and Biology, vol. 32, no. 1, pp. 11–22, 1987. View at Publisher · View at Google Scholar · View at Scopus
  29. P. C. Hansen, Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversio, SIAM Monographs on Mathematical Modeling and Computation, Society for Industrial and Applied Mathematics, Philadelphia, Pa, USA, 1998. View at Zentralblatt MATH
  30. S. C. Jun, J. S. George, J. Paré-Blagoev et al., “Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data,” NeuroImage, vol. 28, no. 1, pp. 84–98, 2005. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Congedo, “Subspace projection filters for real-time brain electromagnetic imaging,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 8, Article ID 1658157, pp. 1624–1634, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. T. E. Ozkurt, M. Sun, W. Jia, and R. J. Sclabassi, “Spatial filtering of MEG signals for user-specified spherical regions,” IEEE Transactions on Bio-Medical Engineering, vol. 56, no. 10, pp. 2429–2438, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. S. Ko and S. C. Jun, “Beamformer for simultaneous magnetoencephalography and electroencephalography analysis,” Journal of Applied Physics, vol. 107, p. 09B315, 2010. View at Publisher · View at Google Scholar