Skip to main content

Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces

  • 1982 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10784)


Brain Computer Interfaces provide a very challenging classification task due to small numbers of instances, large numbers of features, non-stationary problems, and low signal-to-noise ratios. Feature selection (FS) is a promising solution to help mitigate these effects. Wrapper FS methods are typically found to outperform filter FS methods, but reliance on cross-validation accuracies can be misleading due to over-fitting. This paper proposes a filter-wrapper hybrid based on Iterated Local Search and Mutual Information, and shows that it can provide more reliable solutions, where the solutions are more able to generalise to unseen data. This study further contributes comparisons over multiple datasets, something that has been uncommon in the literature.


  • Brain Computer Interface
  • Mutual information
  • Evolutionary search
  • Iterated Local Search

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-77538-8_5
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-77538-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   143.00
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.


  1. 1.

  2. 2.


  1. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016).

    CrossRef  Google Scholar 

  2. Vega, R., Sajed, T., Mathewson, K.W., Khare, K., Pilarski, P.M., Greiner, R., Sanchez-Ante, G., Antelis, J.M.: Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals. Artif. Intell. Res. 1, 37–51 (2016).

    Google Scholar 

  3. Cabrera, A.F., Farina, D., Dremstrup, K.: Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery. Med. Biol. Eng. Compu. 48(2), 123–132 (2010).

    CrossRef  Google Scholar 

  4. Ang, K.K., Chin, Z.Y., Wang, C., Guan, C., Zhang, H.: Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front. Neurosci. 6, 1–9 (2012).

    CrossRef  Google Scholar 

  5. Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A., Ahmad, I.: A review of channel selection algorithms for EEG signal processing. EURASIP J. Adv. Signal Process. 2015(1), 66 (2015).

    CrossRef  Google Scholar 

  6. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948). (July 1928)

    MathSciNet  CrossRef  MATH  Google Scholar 

  7. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005).

    CrossRef  Google Scholar 

  8. Ciaccio, E.J., Dunn, S.M., Akay, M.: Biosignal pattern recognition and interpretation systems: Part 2 of 4: methods for feature extraction and selection. IEEE Eng. Med. Biol. Mag. 12, 106–113 (1993).

    CrossRef  Google Scholar 

  9. Rejer, I.: Genetic algorithm with aggressive mutation for feature selection in BCI feature space. Pattern Anal. Appl. 18(3), 485–492 (2014).

    MathSciNet  CrossRef  Google Scholar 

  10. Wei, Q., Wang, Y.: Binary multi-objective particle swarm optimization for channel selection in motor imagery based brain-computer interfaces. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BME I), pp. 667–670 (2011).

  11. Atyabi, A., Luerssen, M., Fitzgibbon, S.P., Powers, D.M.W.: Use of evolutionary algorithm-based methods in EEG based BCI systems. In: Swarm Intelligence for Electric and Electronic Engineering, pp. 326–344 (2012).

  12. Gan, J.Q., Hasan, B.A.S., Tsui, C.S.L.: A filter-dominating hybrid sequential forward floating search method for feature subset selection in high-dimensional space. Int. J. Mach. Learn. Cybern. 5(3), 413–423 (2014).

    CrossRef  Google Scholar 

  13. Khushaba, R.N., Al-Ani, A., AlSukker, A., Al-Jumaily, A.: A combined ant colony and differential evolution feature selection algorithm. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 1–12. Springer, Heidelberg (2008).

    CrossRef  Google Scholar 

  14. Tan, F., Fu, X., Zhang, Y., Bourgeois, A.G.: A genetic algorithm-based method for feature subset selection. Soft. Comput. 12(2), 111–120 (2008).

    CrossRef  Google Scholar 

  15. Ali, S.I., Shahzad, W.: A feature subset selection method based on symmetric uncertainty and Ant Colony Optimization. In: 2012 International Conference on Emerging Technologies, pp. 1–6 (2012).

  16. Nguyen, H.B., Xue, B., Liu, I., Zhang, M.: Filter based backward elimination in wrapper based PSO for feature selection in classification. In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, pp. 3111–3118 (2014).

  17. Zhu, Z., Jia, S., Ji, Z.: Towards a memetic feature selection paradigm. IEEE Comput. Intell. Mag. 5(2), 41–53 (2010).

    CrossRef  Google Scholar 

  18. Lourenco, H.R., Martin, O.C., Stutzle, T.: Iterated local search: framework and applications. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 363–397. Springer, Boston (2010).

    Google Scholar 

  19. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014).

    CrossRef  Google Scholar 

  20. Lotte, F., Congedo, M., Anatole, L., Lotte, F., Congedo, M., Anatole, L.: A Review of Classification Algorithms for EEG-based BCI (2007).

  21. Ramos, A.C., Vellasco, M.: Feature selection methods applied to motor imagery task classification. In: 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (2016)., ISBN 9781509051052

  22. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Articial Intelligence (IJCAI), vol. 5, pp. 1–7 (1995)., ISBN 1-55860-363-8

  23. Lan, T., Erdogmus, D., Adami, A., Pavel, M., Mathan, S.: Salient EEG channel selection in brain computer interfaces by mutual information maximization. In: Conference proceedings Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 7, pp. 7064–7067. IEEE Engineering in Medicine and Biology Society (2005).

Download references


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

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jason Adair .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Adair, J., Brownlee, A.E.I., Ochoa, G. (2018). Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)