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International Conference on Artificial Neural Networks

ICANN 2001: Artificial Neural Networks — ICANN 2001 pp 609–616Cite as

Single Trial Estimation of Evoked Potentials Using Gaussian Mixture Models with Integrated Noise Component

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2130))

Abstract

Gaussian Mixture Models with integrated noise component are a method developed for speech analysis to estimate signals hidden in background noise. We apply this technique to estimate single trial evoked potentials which are buried in noise up to five times stronger than the signal. An empirical study using artificial data is presented and results are compared to the standard technique of averaging.

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References

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Author information

Authors and Affiliations

  1. The Austrian Research Institute for Artificial Intelligence, Schottengasse 3, A-1010, Vienna, Austria

    Arthur Flexer & Georg Dorffner

  2. Department of Psychology, University of Vienna, Liebiggasse 5, A-1010, Vienna, Austria

    Herbert Bauer & Claus Lamm

Authors
  1. Arthur Flexer
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  2. Herbert Bauer
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  3. Claus Lamm
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  4. Georg Dorffner
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Editor information

Editors and Affiliations

  1. Dept. of Mecidal Cybernetics and Artificial Intelligence, University of Vienna, Freyung 6/2, 1010, Vienna, Austria

    Georg Dorffner

  2. Institute for Computer Aided Automation Pattern Recognition and Image Processing Group, Technical University of Vienna, Favoritenstr. 9/1832, 1040, Vienna, Austria

    Horst Bischof

  3. Institut für Statistik, Wirtschaftsuniversität Wien, Augasse 2-6, 1090, Wien, Austria

    Kurt Hornik

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© 2001 Springer-Verlag Berlin Heidelberg

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Flexer, A., Bauer, H., Lamm, C., Dorffner, G. (2001). Single Trial Estimation of Evoked Potentials Using Gaussian Mixture Models with Integrated Noise Component. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_85

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  • DOI: https://doi.org/10.1007/3-540-44668-0_85

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42486-4

  • Online ISBN: 978-3-540-44668-2

  • eBook Packages: Springer Book Archive

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