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Computational Intelligence and Neuroscience
Volume 2012 (2012), Article ID 275073, 12 pages
http://dx.doi.org/10.1155/2012/275073
Research Article

Noise-Assisted Instantaneous Coherence Analysis of Brain Connectivity

School of Biomedical Engineering, Science & Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA

Received 13 December 2011; Revised 17 February 2012; Accepted 6 March 2012

Academic Editor: Marc Van Hulle

Copyright © 2012 Meng Hu and Hualou Liang. 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. G. G. Gregoriou, S. J. Gotts, H. Zhou, and R. Desimone, “High-Frequency, long-range coupling between prefrontal and visual cortex during attention,” Science, vol. 324, no. 5931, pp. 1207–1210, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Liang, S. L. Bressler, M. Ding, R. Desimone, and P. Fries, “Temporal dynamics of attention-modulated neuronal synchronization in macaque V4,” Neurocomputing, vol. 52–54, pp. 481–487, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Brovelli, M. Ding, A. Ledberg, Y. Chen, R. Nakamura, and S. L. Bressler, “Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality,” Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. 26, pp. 9849–9854, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, New York, NY, USA, 1998.
  5. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A, vol. 454, no. 1971, pp. 903–993, 1998. View at Google Scholar · View at Scopus
  6. T. Tanaka and D. P. Mandic, “Complex empirical mode decomposition,” IEEE Signal Processing Letters, vol. 14, no. 2, pp. 101–104, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Altaf, T. Gautama, T. Tanaka, and D. P. Mandic, “Rotation invariant complex empirical mode decomposition,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '07), pp. 1009–1012, Honolulu, Hawaii, USA, April 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Rilling, P. Flandrin, P. Goncalves, and J. M. Lilly, “Bivariate empirical mode decomposition,” IEEE Signal Processing Letters, vol. 14, no. 12, pp. 936–939, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. N. Rehman and D. P. Mandic, “Empirical mode decomposition for trivariate signals,” IEEE Transaction on Signal Processing, vol. 58, pp. 1059–1068, 2010. View at Google Scholar
  10. Z. Wu, N. E. Huang, and X. Chen, “The multi-dimensional ensemble empirical mode decomposition method,” Advances in Adaptive Data Analysis, vol. 1, pp. 339–372, 2009. View at Google Scholar
  11. N. Rehman and D. P. Mandic, “Multivariate empirical mode decomposition,” Proceedings of the Royal Society A, vol. 466, no. 2117, pp. 1291–1302, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Rehman and D. P. Mandic, “Filter bank property of multivariate empirical mode decomposition,” IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 2421–2426, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Hu and H. Liang, “Adaptive multiscale entropy analysis of multivariate neural data,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp. 12–15, 2011. View at Google Scholar
  14. M. Hu and H. Liang, “Intrinsic mode entropy based on multivariate empirical mode decomposition and its application to neural data analysis,” Cognitive Neurodynamics, vol. 5, no. 3, pp. 277–284, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. C. W. J. Granger, “Investigating causal relations by econometric models and crossspectral methods,” Econometrica, vol. 37, no. 3, pp. 424–438, 1969. View at Google Scholar
  16. M. Dhamala, G. Rangarajan, and M. Ding, “Analyzing information flow in brain networks with nonparametric Granger causality,” NeuroImage, vol. 41, no. 2, pp. 354–362, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Wilke, N. K. Logothetis, and D. A. Leopold, “Generalized flash suppression of salient visual targets,” Neuron, vol. 39, no. 6, pp. 1043–1052, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. A. V. Oppenheim and R. W. Schafer, Digital Signal Processing, Prentice Hall, Englewood Cliffs, NJ, USA, 1989.
  19. W. J. Freeman, “Origin, structure, and role of background EEG activity—part 1. Analytic amplitude,” Clinical Neurophysiology, vol. 115, no. 9, pp. 2077–2088, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Kaminski and H. Liang, “Causal influence: advances in neurosignal analysis,” Critical Reviews in Biomedical Engineering, vol. 33, no. 4, pp. 347–430, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Maris and R. Oostenveld, “Nonparametric statistical testing of EEG- and MEG-data,” Journal of Neuroscience Methods, vol. 164, no. 1, pp. 177–190, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. G. M. Jenkins and D. G. Watts, Spectral Analysis and Its Applications, Holden-day, San Francisco, Calif, USA, 1968.
  23. Z. Wang, A. Maier, D. A. Leopold, N. K. Logothetis, and H. Liang, “Single-trial evoked potential estimation using wavelets,” Computers in Biology and Medicine, vol. 37, no. 4, pp. 463–473, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Wilke, N. K. Logothetis, and D. A. Leopold, “Local field potential reflects perceptual suppression in monkey visual cortex,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 46, pp. 17507–17512, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. D. Maraun, J. Kurths, and M. Holschneider, “Nonstationary Gaussian processes in wavelet domain: synthesis, estimation, and significance testing,” Physical Review E, vol. 75, no. 1, Article ID 016707, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. D. Maraun and J. Kurths, “Cross wavelet analysis: significance testing and pitfalls,” Nonlinear Processes in Geophysics, vol. 11, no. 4, pp. 505–514, 2004. View at Google Scholar · View at Scopus
  27. P. Flandrin, G. Rilling, and P. Goncalves, “Empirical mode decomposition as a filter bank,” IEEE Signal Processing Letters, vol. 11, no. 2, pp. 112–114, 2004. View at Publisher · View at Google Scholar · View at Scopus
  28. Z. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society A, vol. 460, no. 2046, pp. 1597–1611, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. P. Flandrin, P. Goncalves, and G. Rilling, “EMD equivalent filter banks, from interpretation to applications,” in Hilbert-Huang Transform : Introduction and Applications, World Scientific, Singapore, 2005. View at Google Scholar
  30. Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, pp. 1–41, 2009. View at Google Scholar
  31. C. M. Sweeney-Reed and S. J. Nasuto, “A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition,” Journal of Computational Neuroscience, vol. 23, no. 1, pp. 79–111, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Palus and A. Stefanovska, “Direction of coupling from phases of interacting oscillators: an information-theoretic approach,” Physical Review E, vol. 67, no. 5, Article ID 055201, 2003. View at Google Scholar · View at Scopus
  33. M. G. Rosenblum and A. S. Pikovsky, “Detecting direction of coupling in interacting oscillators,” Physical Review E, vol. 64, no. 4, Article ID 045202R, 2001. View at Google Scholar · View at Scopus
  34. M. Palus, D. Novotna, and P. Tichavsky, “Shifts of seasons at the European mid-latitudes: natural fluctuations correlated with the North Atlantic Oscillation,” Geophysical Research Letters, vol. 32, Article ID L12805, 2005. View at Google Scholar