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Classification of EEG Signals Using Vector Quantization

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8468))

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Abstract

Proper identification and classification of the EEG data still pauses a problem in the field of brain diagnosis. However, the application of such algorithm is almost unlimited as they may be involved in applications such as, brain computer interface for controlling of prosthesis, wheelchair, etc.. In this paper we are focusing on applying data compression in the classification of EEG signals. We combine a vector quantization and the normalized compression distance for proper classification of a finger movement data.

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Author information

Authors and Affiliations

  1. Department of Computer Science, FEECS, IT4 Innovations, Centre of Excellence, VSB-Technical University of Ostrava, Ostrava Poruba, Czech Republic

    Petr Berek, Michal Prilepok, Jan Platos & Vaclav Snasel

Authors
  1. Petr Berek
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  2. Michal Prilepok
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  3. Jan Platos
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  4. Vaclav Snasel
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Editor information

Editors and Affiliations

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

    Leszek Rutkowski  & Rafał Scherer  & 

  2. Częstochowa University of Technology, 42-200, Częstochowa, Poland

    Marcin Korytkowski

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

    Ryszard Tadeusiewicz

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

    Lotfi A. Zadeh

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

    Jacek M. Zurada

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© 2014 Springer International Publishing Switzerland

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Berek, P., Prilepok, M., Platos, J., Snasel, V. (2014). Classification of EEG Signals Using Vector Quantization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-07176-3_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07175-6

  • Online ISBN: 978-3-319-07176-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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