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Class-Adaptive Denoising for EEG Data Classification

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

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Abstract

Brain-computer interface (BCI) systems use electro-encephalogram (EEG) data to control external electronic devices. The main task of BCI systems is to differentiate the classes of mental tasks from the EEG data. The EEG data is inherently complex and difficult to analyze due to interference by eye and muscle movements as well as electrical grid noise. In this paper we analyze shrinkage functions for signal filtering and propose a class-adaptive method for EEG data denoising. The results are evaluated using a Support Vector Machine.

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Author information

Authors and Affiliations

  1. Software Engineering Department, Kaunas University of Technology, Studentų 50-415, Kaunas, Lithuania

    Ignas Martišius & Robertas Damaševičius

Authors
  1. Ignas Martišius
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  2. Robertas Damaševičius
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Martišius, I., Damaševičius, R. (2012). Class-Adaptive Denoising for EEG Data Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_36

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  • DOI: https://doi.org/10.1007/978-3-642-29350-4_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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