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Relevant Feature Selection from EEG Signal for Mental Task Classification

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Advances in Knowledge Discovery and Data Mining (PAKDD 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7302))

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Abstract

In last few years, the research community has shown interest in the development of Brain Computer Interface which may assists physically challenged people to communicate with the help of brain signal. The two important components of such BCI system are to determine appropriate features and classification method to achieve better performance. In literature, Empirical Mode Decomposition is suggested for feature extraction from EEG which is suitable for the analysis of non-linear and non-stationary time series. However, the features obtained from EEG may contain irrelevant and redundant features which make them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalization. Hence, relevant features which provide maximum classification accuracy are selected using ratio of scatter matrices, Chernoff distance measure and linear regression. The performance of different mental task using different measures used for feature selection is compared and evaluated in terms of classification accuracy. Experimental results show that there is significant improvement in classification accuracy with features selected using all feature selection methods and in particular with ratio of scatter matrices.

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Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India, 1110067

    Akshansh Gupta & R. K. Agrawal

Authors
  1. Akshansh Gupta
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  2. R. K. Agrawal
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, 48824-1226, East Lansing, MI, USA

    Pang-Ning Tan

  2. School of Information Technologies, University of Sydney, 1 Cleveland St., 2006, Sydney, NSW, Australia

    Sanjay Chawla

  3. Faculty of Computing and Informatics, Jalan Multimedia, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Chin Kuan Ho

  4. Department of Computing and Information Systems, The University of Melbourne, 111 Barry Street, 3053, Melbourne, VIC, Australia

    James Bailey

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© 2012 Springer-Verlag Berlin Heidelberg

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Gupta, A., Agrawal, R.K. (2012). Relevant Feature Selection from EEG Signal for Mental Task Classification. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30220-6_36

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  • DOI: https://doi.org/10.1007/978-3-642-30220-6_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30219-0

  • Online ISBN: 978-3-642-30220-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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