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Computational Intelligence and Neuroscience
Volume 2011, Article ID 982185, 11 pages
http://dx.doi.org/10.1155/2011/982185
Research Article

Highly Automated Dipole EStimation (HADES)

1Dipartimento di Matematica, Università di Genova, 16126 Genova, Italy
2Sezione di Fisiologia, Dipartimento di Neuroscienze, Università di Parma, 143121 Parma, Italy

Received 29 July 2010; Revised 5 November 2010; Accepted 17 January 2011

Academic Editor: Sylvain Baillet

Copyright © 2011 C. Campi et al. 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. L. Stenbacka, S. Vanni, K. Uutela, and R. Hari, “Comparison of minimum current estimate and dipole modeling in the analysis of simulated activity in the human visual cortices,” NeuroImage, vol. 16, no. 4, pp. 936–943, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Liljestrom, J. Kujala, O. Jensen, and R. Salmelin, “Neuromagnetic localization of rythimc activity in the human brain: a comparison of three methods,” NeuroImage, vol. 25, pp. 734–745, 2005. View at Google Scholar
  3. E. F. Chang, S. S. Nagarajan, M. Mantle, N. M. Barbaro, and H. E. Kirsch, “Magnetic source imaging for the surgical evaluation of electroencephalography-confirmed secondary bilateral synchrony in intractable epilepsy: clinical article,” Journal of Neurosurgery, vol. 111, no. 6, pp. 1248–1256, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. J. C. Mosher, P. S. Lewis, and R. M. Leahy, “Multiple dipole modeling and localization from spatio-temporal MEG data,” IEEE Transactions on Biomedical Engineering, vol. 39, no. 6, pp. 541–557, 1992. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Sorrentino, L. Parkkonen, A. Pascarella, C. Campi, and M. Piana, “Dynamical MEG source modeling with multi-target bayesian filtering,” Human Brain Mapping, vol. 30, no. 6, pp. 1911–1921, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Pascarella, A. Sorrentino, C. Campi, and M. Piana, “Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms,” Inverse Problems and Imaging, vol. 4, no. 1, pp. 169–170, 2010. View at Publisher · View at Google Scholar
  7. I. Molchanov, Theory of Random Sets, Springer, Berlin, Germany, 2005.
  8. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174–188, 2002. View at Publisher · View at Google Scholar
  9. M. S. Hämäläinen and R. J. Ilmoniemi, “Interpreting magnetic fields of the brain: minimum norm estimates,” Medical and Biological Engineering and Computing, vol. 32, no. 1, pp. 35–42, 1994. View at Google Scholar · View at Scopus
  10. A. M. Dale and M. I. Sereno, “Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach,” Journal of Cognitive Neuroscience, vol. 5, no. 2, pp. 162–176, 1993. View at Google Scholar · View at Scopus
  11. A. M. Dale, B. Fischl, and M. I. Sereno, “Cortical surface-based analysis: I. Segmentation and surface reconstruction,” NeuroImage, vol. 9, no. 2, pp. 179–194, 1999. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Campi, A. Pascarella, A. Sorrentino, and M. Piana, “A Rao-Blackwellized particle filter for magnetoencephalography,” Inverse Problems, vol. 24, no. 2, Article ID 025023, 2008. View at Publisher · View at Google Scholar · View at Scopus