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A Signal-Dependent Quadratic Time Frequency Distribution for Neural Source Estimation

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

A novel method for kernel design of a quadratic time frequency distribution (TFD) as the initial step for neural source estimation is proposed. The kernel is constructed based on the product ambiguity function (AF), which efficiently suppresses cross terms and noise in the ambiguity domain. In order to reduce the influence from the strong signal to the weak signal, an iterative approach is implemented. Simulation results validate the method and demonstrate suppression of cross terms and noise, and high resolution in the time frequency domain.

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References

  1. Sun, M., Qian, S., Yan, X., Baumann, S.B., Xia, X.G., Dahl, R.E., Ryan, N.D., Sclabassi, R.J.: Localizing Functional Activity In The Brain Through Time-Frequency Analysis and Synthesis of The EEG. Proc. IEEE 84(9), 1302–1311 (1996)

    Article  Google Scholar 

  2. Sekihara, K., Nagarajan, S., Poeppel, D., Miyashita, Y.: Time-Frequency MEG-MUSIC Algorithm. IEEE Trans. Medical Imaging 18(1), 92–97 (1999)

    Article  Google Scholar 

  3. Sekihara, K., Nagarajan, S., Poeppel, D., Miyauchi, S., Fujimaki, N., Koizumi, H., Miyashita, Y.: Estimating Neural Sources From Each Time-Frequency Component of Magnetoencephalographic Data. IEEE Trans. Biomedical Engineering 47(5), 642–653 (2000)

    Article  Google Scholar 

  4. Choi, H.I., Williams, W.J.: Improved Time-Frequency Representation of Multicomponent Signals Using the Exponential Kernels. IEEE Trans. Signal Processing 37(6), 862–871 (1989)

    Article  Google Scholar 

  5. Barkat, B., Boashash, B.: A High-Resolution Quadratic Time-Frequency Distribution For Multicomponent Signal Analysis. IEEE Trans. Signal Processing 49(10), 2232–2239 (2001)

    Article  Google Scholar 

  6. Ristic, B., Boashash, B.: Kernel Design For Time-Frequency Signal Analysis Using Radon Transform. IEEE Trans. Signal Processing 41(5), 1996–2008 (1993)

    Article  MATH  Google Scholar 

  7. Baraniuk, R.G., Jones, D.L.: A Radially Gaussian Signal Dependent Time Frequency Representation. In: Proc. IEEE Int. Conf. Acoust. Speech, Signal Processing, vol. 5, pp. 3181–3184. IEEE Press, Los Alamitos (1991)

    Google Scholar 

  8. Cohen, L.: Time-Frequency Analysis. Prentice Hall, Englewood Cliffs (1995)

    Google Scholar 

  9. Barbarossa, S., Scaglione, A., Giannakis, G.B.: Product High-order Ambiguity Function For Multicomponent Polynomial-Phase Signal Modeling. IEEE Trans. Signal Processing 46(3), 691–708 (1998)

    Article  Google Scholar 

  10. Ikram, M.Z., Abed-Meraim, K., Hua, Y.: Estimating The Parameters of Chirp Signals: An Iterative Approach. IEEE Trans. Signal Processing 46(12), 3436–3441 (1998)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, P.R. China

    Pu Wang & Jianyu Yang

  2. Blind Source Separation Research Group, University of Electronic Science and Technology of China, Chengdu, 610054, P.R. China

    Pu Wang, Zhi-Lin Zhang, Gang Wang & Quanyi Mo

Authors
  1. Pu Wang
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  2. Jianyu Yang
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  3. Zhi-Lin Zhang
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  4. Gang Wang
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  5. Quanyi Mo
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, P., Yang, J., Zhang, ZL., Wang, G., Mo, Q. (2006). A Signal-Dependent Quadratic Time Frequency Distribution for Neural Source Estimation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_103

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  • DOI: https://doi.org/10.1007/11760023_103

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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