Abstract
This paper presents several improvements to the framework of information-preserving empirical mode decomposition (EMD). The basic framework was presented in our previous work [1]. The method decomposes a non-stationary neural response into a number of oscillatory modes varying in information content. After the spectral information analysis only few modes, taking part in stimulus coding, are retrieved for further analysis. The improvements and enhancement have been proposed for the steps involved in information quantification and modes extraction. An investigation has also been carried out for compression of retrieved informative modes of the neural signal in order to achieve a lower bit rate using the proposed framework. Experimental results are presented.
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References
Mehboob, Z., Yin, H.: Information preserving empirical mode decomposition for filtering field potentials. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 226–233. Springer, Heidelberg (2009)
Rilling, G., Flandrin, P., Gonalves, P.: On empirical mode decomposition and its algorithms (2003), http://perso.ens-lyon.fr/patrick.flandrin/nsip03.pdf
Flandrin, P., Rilling, G., Goncalv‘es, P.: Empirical mode decomposition as a filter bank. IEEE Sig. Proc. Lett, 112–114 (2004)
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. Proc. Royal Soc. 454, 903–995 (1998)
Percival, D.B., Walden, A.T.: Spectral analysis for physical applications: multitapper and conventional univariate techniques. Cambridge University Press, Cambridge (1993)
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)
Hämäläinen, M., Hari, R., Ilmoniemi, R.J., Knuutila, J., Lounasmaa, O.V.: Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65(2), 413–497 (1993)
Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M., Logothetis, N., Panzeri, S.: Low frequency local field potentials and spikes in primary visual cortex convey independent visual information. J. Neurosci. 28(22), 5696–5709 (2008)
Rodrigo, Q.Q., Panzeri, S.: Extracting information from neuronal populations: information theory and decoding approaches. Nat. Rev. Neurosci. 10(3), 173–185 (2009)
Magri, C., Whittingstall, K., Singh, V., Logothetis, N., Panzeri, S.: A toolbox for the fast information analysis of multiple-site lfp, eeg and spike train recordings. BMC Neuroscience 10(81), 1–24 (2009)
Gunduz, A., Principe, J.C.: Correntropy as a novel measure for nonlinearity tests. Signal Processing 89, 14–23 (2009)
Liu, W., Pokharel, P.P., Principe, J.C.: Correntropy: A localized similarity measure. In: IJCNN, pp. 4919–4924 (2006)
Khaldi, K., Boudraa, A.O., Turk, M., Chonave, T., Samaali, I.: Audio encoding based on empirical mode decomposition. In: EUSIPCO, pp. 924–928 (2009)
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Mehboob, Z., Yin, H. (2010). Neural Data Analysis and Reduction Using Improved Framework of Information-Preserving EMD. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_44
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DOI: https://doi.org/10.1007/978-3-642-15381-5_44
Publisher Name: Springer, Berlin, Heidelberg
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