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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 8404565, 10 pages
http://dx.doi.org/10.1155/2016/8404565
Research Article

High-Resolution Cortical Dipole Imaging Using Spatial Inverse Filter Based on Filtering Property

1Graduate School of Science and Technology, Niigata University, Niigata 950-2181, Japan
2Terumo Corporation, Tokyo, Japan

Received 12 April 2016; Revised 15 July 2016; Accepted 7 August 2016

Academic Editor: Joao P. Papa

Copyright © 2016 Junichi Hori and Shintaro Takasawa. 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. R. D. Sidman, M. R. Ford, G. Ramsey, and C. Schlichting, “Age-related features of the resting and P300 auditory evoked responses using the dipole localization method and cortical imaging technique,” Journal of Neuroscience Methods, vol. 33, no. 1, pp. 23–32, 1990. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Wang and B. He, “A computer simulation study of cortical imaging from scalp potentials,” IEEE Transactions on Biomedical Engineering, vol. 45, no. 6, pp. 724–735, 1998. View at Publisher · View at Google Scholar · View at Scopus
  3. A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-Posed Problems, Halsted Press, New York, NY, USA, 1977. View at MathSciNet
  4. P. C. Hansen, “The truncated SVD as a method for regularization,” BIT Numerical Mathematics, vol. 27, no. 4, pp. 534–553, 1987. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. J. Hori and B. He, “Equivalent dipole source imaging of brain electric activity by means of parametric projection filter,” Annals of Biomedical Engineering, vol. 29, no. 5, pp. 436–445, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Hori, M. Aiba, and B. He, “Spatio-temporal cortical source imaging of brain electrical activity by means of time-varying parametric projection filter,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 5, pp. 768–777, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. R. D. Fierro, G. H. Golub, P. C. Hansen, and D. P. O'Leary, “Regularization by truncated total least squares,” SIAM Journal on Scientific Computing, vol. 18, no. 4, pp. 1223–1241, 1997. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. X. He, J. Liang, X. Qu, H. Huang, Y. Hou, and J. Tian, “Truncated total least squares method with a practical truncation parameter choice scheme for bioluminescence tomography inverse problem,” International Journal of Biomedical Imaging, vol. 2010, Article ID 291874, 11 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Shou, L. Xia, M. Jiang, Q. Wei, F. Liu, and S. Crozier, “Truncated total least squares: a new regularization method for the solution of ECG inverse problems,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 4, pp. 1327–1335, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Hori and K. Takeuchi, “Cortical dipole imaging using truncated total least squares considering transfer matrix error,” in Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '13), pp. 5410–5413, IEEE, Osaka, Japan, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Takasawa and J. Hori, “Improvement of cortical dipole imaging based on filtering property approximated by sigmoid function,” IEICE Technical Reports, vol. 115, no. 229, pp. 1–6, 2015. View at Google Scholar
  12. S. Takasawa and J. Hori, “Improvement of cortical dipole imaging based on filtering property,” IEICE Technical Report, vol. 114, no. 213, pp. 11–16, 2014. View at Google Scholar
  13. J. Hori and Y. Watanabe, “Cortical dipole imaging for multiple signal sources considering time-varying non-uniform noise,” IEEJ Transactions on Electronics, Information and Systems, vol. 131, no. 11, pp. 1958–1965, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. N. P. Subramaniyam, O. R. M. Väisänen, K. E. Wendel, and J. A. V. Malmivuo, “Cortical potential imaging using L-curve and GCV method to choose the regularisation parameter,” Nonlinear Biomedical Physics, vol. 4, no. 1, article 4, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. P. C. Hansen and D. P. O'Leary, “The use of the L-curve in the regularization of discrete ill-posed problems,” SIAM Journal on Scientific Computing, vol. 14, no. 6, pp. 1487–1503, 1993. View at Publisher · View at Google Scholar · View at MathSciNet
  16. Y. Hosoda and T. Kitagawa, “Optimum regularization for ill-posed problems by means of L-curve,” The Japan Society for Industrial and Applied Mathematics, vol. 2, no. 1, pp. 55–67, 1992. View at Google Scholar
  17. J. Lian and B. He, “A minimal product method and its application to cortical imaging,” Brain Topography, vol. 13, no. 3, pp. 209–217, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. J. L. Castellanos, S. Gómez, and V. Guerra, “The triangle method for finding the corner of the L-curve,” Applied Numerical Mathematics, vol. 43, no. 4, pp. 359–373, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. G. Rodriguez and D. Theis, “An algorithm for estimating the optimal regularization parameter by the L-curve,” Rendiconti di Matematica, Serie VII, vol. 25, no. 1, pp. 69–84, 2005. View at Google Scholar · View at MathSciNet
  20. P. C. Hansen, T. K. Jensen, and G. Rodriguez, “An adaptive pruning algorithm for the discrete L-curve criterion,” Journal of Computational and Applied Mathematics, vol. 198, no. 2, pp. 483–492, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. M. W. Eyesenck and M. T. Keane, Cognitive Psychology: A Student's Handbook, Psychology Press, 2010.
  22. B. He, X. Zhang, J. Lian, H. Sasaki, D. Wu, and V. L. Towle, “Boundary element method-based cortical potential imaging of somatosensory evoked potentials using subjects' magnetic resonance images,” NeuroImage, vol. 16, no. 3, pp. 564–576, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Hori, T. Miwa, T. Ohshima, and B. He, “Cortical dipole imaging of movement-related potentials by means of parametric inverse filters incorporating with signal and noise covariance,” Methods of Information in Medicine, vol. 46, no. 2, pp. 242–246, 2007. View at Google Scholar · View at Scopus