Information Preserving Empirical Mode Decomposition for Filtering Field Potentials

  • Zareen Mehboob
  • Hujun Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


This paper presents a concept of using the empirical mode decomposition (EMD) as a filtering tool to extract information-preserving intrinsic mode functions (IMFs) in an adaptive manner. The approach is tested on several local field potentials (LFPs) and information quantification is carried out in spectral domain using Shannon’s Information. The study suggests that not all IMFs are information carriers. It is found that the 1st IMF carries 60-80% of the total information from original LFP and few informative IMFs are usually the main information carriers. Adding more IMFs does not increase the information level. For different datasets, the order of the informative IMFs varies and by using information preserving EMD, only few IMFs are retained to provide a simplified representation of underlying oscillations contained in LFPs.


Support Vector Machine Mutual Information Wavelet Transform Empirical Mode Decomposition Information Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Katzner, S., Nauhaus, I., Benucci, A., Bonin, V., Ringach, D., Carandini, M.: The local field potential in primary visual cortex: how local is it? Journal of Vision 7(15), 72 (2007)CrossRefGoogle Scholar
  2. 2.
    Legatt, A.D., Arezzo, J., Vaughan, H.: Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: effects of volume-conducted potentials. Journal of Neuroscience Methods 2(2), 203–217 (1980)CrossRefGoogle Scholar
  3. 3.
    Kreiman, G., Hung, C.P., Kraskov, A., Quiroga, R.Q., Poggio, T., DiCarlo, J.: Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex. Neuron 49(3), 433–445 (2006)CrossRefGoogle Scholar
  4. 4.
    Zeitler, M., Fries, P., Gielen, S.: Assessing neuronal coherence with single-unit, multi-unit and local field potentials. Neural Computation 18, 2256–2281 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Victor, J.D.: Approaches to information-theoretic analysis of neural activity 1(3), 302–316 (2006)Google Scholar
  6. 6.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society 454, 903–995 (1998)Google Scholar
  7. 7.
    Mandic, D.P., Souretis, G., Leong, W.Y., Looney, D., van Hulle, M.M., Tanaka, T.: Complex Empirical Mode Decomposition for Multichannel Information Fusion. In: Signal Processing Techniques for Knowledge Extraction and Information Fusion, pp. 243–260. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Weng, B., Xuan, G., Kolodzey, J., Barner, K.: Empirical mode decomposition as a tool for DNA sequence analysis from the terahertz spectroscopy measurements. In: IEEE Int. Workshop on Genomic Signal Processing and Statistics, pp. 63–64 (2006)Google Scholar
  9. 9.
    Rilling, G., Flandrin, P., Gonçalves, P.: On empirical mode decomposition and its algorithms (2003),
  10. 10.
    Liang, H., Bressler, S., Buffalo, E., Desimone, R., Fries, P.: Empirical mode decomposition of local field potentials from macaque V4 in visual spatial attention. Biological Cybernetics 92(6), 380–392 (2005)CrossRefzbMATHGoogle Scholar
  11. 11.
    Manyakov, N.V., van Hulle, M.M.: Synchronization in monkey visual cortex analyzed with an information-theoretic measure. Chaos 18, 1–7 (2008)CrossRefGoogle Scholar
  12. 12.
    Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 623–656 (1948)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Cover, T., Thomas, J.: Elements of Infomation Theory. Wiley Interscience, Hoboken (1999)Google Scholar
  14. 14.
    Percival, D.B., Walden, A.T.: Spectral Analysis for Physical Applications:Multitaper and Conventional Univariate Techniques. Cambridge University Press, Cambridge (1993)CrossRefzbMATHGoogle Scholar
  15. 15.
    Multi-Taper Method in Matlab:
  16. 16.
    Manyakov, N.V., van Hulle, M.M.: Discriminating visual stimuli from local field potentials recorded with a multi-electrode array in the monkey’s visual cortex. In: IEEE Workshop on Machine Learning for Signal Processing (2008)Google Scholar
  17. 17.
  18. 18.
    Chang, C.C., Lin, C.J.: LIBSVM (2001),
  19. 19.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A pratical guide to support vector machine,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zareen Mehboob
    • 1
  • Hujun Yin
    • 1
  1. 1.The University of ManchesterUK

Personalised recommendations