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Advances in Computational Intelligence Systems pp 287–307Cite as

Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 513))

Abstract

Brain Computer Interfaces are an essential technology for the advancement of prosthetic limbs, but current signal acquisition methods are hindered by a number of factors, not least, noise. In this context, Feature Selection is required to choose the important signal features and improve classifier accuracy. Evolutionary algorithms have proven to outperform filtering methods (in terms of accuracy) for Feature Selection. This paper applies a single-point heuristic search method, Iterated Local Search (ILS), and compares it to a genetic algorithm (GA) and a memetic algorithm (MA). It then further attempts to utilise Linkage between features to guide search operators in the algorithms stated. The GA was found to outperform ILS. Counter-intuitively, linkage-guided algorithms resulted in higher classification error rates than their unguided alternatives. Explanations for this are explored.

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Notes

  1. 1.

    http://www.bbci.de/competition/iii/#datasets.

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Acknowledgments

Work funded by UK EPSRC grant EP/J017515 (DAASE).

Author information

Authors and Affiliations

  1. Department of Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, Scotland, UK

    Jason Adair, Alexander Brownlee & Gabriela Ochoa

Authors
  1. Jason Adair
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  2. Alexander Brownlee
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  3. Gabriela Ochoa
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Corresponding author

Correspondence to Jason Adair .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg School of Computing and Communications, Lancaster, United Kingdom

    Plamen Angelov

  2. School of Computing, University of Portsmouth School of Computing, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  3. School of Comp. Sci. & Digital Media, Robert Gordon University School of Comp. Sci. & Digital Media, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth University Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth, United Kingdom

    Qiang Shen

Appendix

Appendix

See Table 2.

Table 2 Table displaying each feature referenced by the channel, second and frequency from which it was extracted
Full size table

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Adair, J., Brownlee, A., Ochoa, G. (2017). Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces. In: Angelov, P., Gegov, A., Jayne, C., Shen, Q. (eds) Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_19

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  • DOI: https://doi.org/10.1007/978-3-319-46562-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46561-6

  • Online ISBN: 978-3-319-46562-3

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