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Dynamic Time Warping Based on Modified Alignment Costs for Evoked Potentials Averaging

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Man–Machine Interactions 4

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 391))

Abstract

Averaging of time-warped signal cycles is an important tool for suppressing noise of quasi-periodic or event related signals. However, in the paper we show that the operation of time warping introduces unfavorable correlation among the noise components of the summed cycles. Such correlation violates the requirements necessary for effective averaging and results in poor suppression of noise. To limit these effects, we redefine the matrix of the alignment costs. The proposed modifications result in significant increase of the noise reduction factor in the experiments on different types and levels of noise.

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Acknowledgments

This research was partially supported by statutory funds (BK-2015, BKM-2015) of the Institute of Electronics, Silesian University of Technology and GeCONiI project (T. Moroń). The work was performed using the infrastructure supported by POIG.02.03.01-24-099/13 grant: GeCONiI–Upper Silesian Center for Computational Science and Engineering.

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Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, Gliwice, Poland

    Marian Kotas, Jacek M. Leski & Tomasz Moroń

Authors
  1. Marian Kotas
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  2. Jacek M. Leski
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  3. Tomasz Moroń
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Corresponding author

Correspondence to Marian Kotas .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Brachman

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

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Kotas, M., Leski, J.M., Moroń, T. (2016). Dynamic Time Warping Based on Modified Alignment Costs for Evoked Potentials Averaging. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-23437-3_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23436-6

  • Online ISBN: 978-3-319-23437-3

  • eBook Packages: EngineeringEngineering (R0)

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