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Feature Selection for Brain-Computer Interfaces

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New Frontiers in Applied Data Mining (PAKDD 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5669))

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Abstract

In this paper we empirically evaluate feature selection methods for classification of Brain-Computer Interface (BCI) data. We selected five state-of the-art methods, suitable for the noisy, correlated and highly dimensional BCI data, namely: information gain ranking, correlation-based feature selection, ReliefF, consistency-based feature selection and 1R ranking. We tested them with ten classification algorithms, representing different learning paradigms, on a benchmark BCI competition dataset. The results show that all feature selectors significantly reduced the number of features and also improved accuracy when used with suitable classification algorithms. The top three feature selectors in terms of classification accuracy were correlation-based feature selection, information gain and 1R ranking, with correlation based feature selection choosing the smallest number of features.

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Author information

Authors and Affiliations

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

    Irena Koprinska

Authors
  1. Irena Koprinska
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Editor information

Editors and Affiliations

  1. Thammasat University, Sirindhorn International Institute of Technology,, 131 Moo 5 Tiwanont Road, Bangkadi, 12000, Muang, Pathumthani, Thailand

    Thanaruk Theeramunkong

  2. Department of Architecture for Intelligence, The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka,Ibaraki, 567-0047, Osaka, Japan

    Cholwich Nattee

  3. Center for Informatics, Federal University of Pernambuco, Brazil

    Paulo J. L. Adeodato

  4. Computer Science and Engineering Department, University of Notre Dame, 353 Fitzpatrick Hall, 46556, Notre Dame, IN, USA

    Nitesh Chawla

  5. Department of Computer Science, The Australian National University, Australia

    Peter Christen

  6. TELECOM Bretagne, Lab-STICC, Institut TELECOM, Brest, France

    Philippe Lenca

  7. School of Information Technologies, University of Sydney, P.O. Box, Australia

    Josiah Poon

  8. Australian Taxation Office, Australia

    Graham Williams

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© 2010 Springer-Verlag Berlin Heidelberg

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Koprinska, I. (2010). Feature Selection for Brain-Computer Interfaces. In: Theeramunkong, T., et al. New Frontiers in Applied Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14640-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-14640-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14639-8

  • Online ISBN: 978-3-642-14640-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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