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A Method for Fast Estimation of Evoked Potentials Based on Independent Component Analysis

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Book cover 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

Independent component analysis (ICA) is a new powerful tool for blind source separation. This paper proposes a new algorithm that combines two existent algorithms, the improved infomax algorithm and the fastICA algorithm. Utilizing the initial weights obtained by the improved infomax algorithm, we can not only reduce the length of data which fastICA algorithm needs, but also enhance the convergence stability of fastICA algorithm. The effectiveness of the algorithm is verified by computer simulations.

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References

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116024, Dalian, P. R China

    Ting Li, Tianshuang Qiu, Xuxiu Zhang, Anqing Zhang & Wenhong Liu

Authors
  1. Ting Li
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  2. Tianshuang Qiu
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  3. Xuxiu Zhang
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  4. Anqing Zhang
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  5. Wenhong Liu
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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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Li, T., Qiu, T., Zhang, X., Zhang, A., Liu, W. (2004). A Method for Fast Estimation of Evoked Potentials Based on Independent Component Analysis. 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_80

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_80

  • 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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