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Brain Signals: Feature Extraction and Classification Using Rough Set Methods

  • Conference paper
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3642))

Abstract

A brain computer interface (BCI) makes it possible to monitor conscious brain electrical activity, via electroencephalogram (EEG) signals, and detecting characteristics of brain signal patterns, via digital signal processing algorithms. Event Related Potentials (ERPs) are measures that reflect the responses of the brain to events in the external or internal environment of the organism. P300 is the most important and the most studied component of the ERP. In this paper, a new method for P300 wave detection is introduced. It consists of two components: feature extraction and classification. For feature extraction, Mexican hat wavelet coefficients provide robust features when averaged over different scales. Classification has been carried out using rough set based methods. The overall results show that P300 wave detection can be performed using only five EEG channels. This in turn reduces the computational time compared to the averaging method that uses more channels.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada

    Reza Fazel-Rezai

  2. Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, R3B 2E9, Canada

    Sheela Ramanna

Authors
  1. Reza Fazel-Rezai
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  2. Sheela Ramanna
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2 Canada, Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, P.O. Box, Poland

    Dominik Ślęzak

  2. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    JingTao Yao  & Wojciech Ziarko  & 

  3. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  4. College of Computer and Information Engineering, Hehan University, Henan, China

    Xiaohua Hu

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© 2005 Springer-Verlag Berlin Heidelberg

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Fazel-Rezai, R., Ramanna, S. (2005). Brain Signals: Feature Extraction and Classification Using Rough Set Methods. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_75

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  • DOI: https://doi.org/10.1007/11548706_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28660-8

  • Online ISBN: 978-3-540-31824-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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