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Tracking the Amplitude Variation of Evoked Potential by ICA and WT

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

Evoked potential (EP) is non-stationary during the recording of electroencephalograph (EEG). This paper promotes a method to track the variation of EP’s amplitude by the application of independent component analysis (ICA) and wavelet transform (WT). The utilization of the spatial information and multi-trial recording improves the signal-to-noise ratio (SNR) greatly. The variation trend of EP’s amplitude across trials can be evaluated quantitatively. Our result on real auditory evoked potential shows a drop of about 40% on the amplitude of EP during 10 minutes recording. The present work is helpful to study the uncertainty and singularity of EP. Furthermore, it will put forward the reasonable experiment design of EP extraction.

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References

  1. Wei, Q., Kenneth, S.M.F., Francis, H.Y.C., et al.: Adaptive Filtering of EPs with Radial-basisfunction Neural Network prefilter. IEEE Trans. Biomedical Engineering 3, 224–232 (2002)

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Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, P. R. China

    Haiyan Ding & Datian Ye

Authors
  1. Haiyan Ding
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  2. Datian Ye
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Ding, H., Ye, D. (2004). Tracking the Amplitude Variation of Evoked Potential by ICA and WT. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_73

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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