Advertisement

A Label-Aided Filter Method for Multi-objective Feature Selection in EEG Classification for BCI

  • Pedro Martín-Smith
  • Julio OrtegaEmail author
  • Javier Asensio-Cubero
  • John Q. Gan
  • Andrés Ortiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

Abstract

This paper proposes and evaluates a filter approach for evolutionary multi-objective feature selection in classification problems with a large number of features. Such classification problems frequently appear in many bioinformatics applications where the number of patterns is smaller than the number of features and thus the curse of dimensionality problem exists. The main contribution of this paper is proposing a set of label-aided utility functions that allows the effective search of the most adequate subset of features through an evolutionary multi-objective optimization scheme. The experimental results have been obtained in a brain-computer interface (BCI) classification task based on LDA classifiers, where the properties of multi-resolution analysis (MRA) for signal analysis in temporal and spectral domains have been used to extract the features from EEG signals. The results from the proposed filter method demonstrate some advantages such as less time consumption and better generalization capabilities with respect to some wrapper-based multi-objective feature selection alternatives.

Keywords

Brain-Computer Interfaces (BCI) Filter methods Feature selection Multi-objective optimization Multi-Resolution Analysis (MRA) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)CrossRefGoogle Scholar
  2. 2.
    Sima, C., Dougherty, E.: What should be expected from feature selection in small-sample settings. Bioinformatics 22, 2430–2436 (2006)CrossRefGoogle Scholar
  3. 3.
    Acır, N., Güzeliş, C.: An application of support vector machine in bioinformatics: automated recognition of epileptiform patterns in EEG using SVM classifier designed by a perturbation method. In: Yakhno, T. (ed.) ADVIS 2004. LNCS, vol. 3261, pp. 462–471. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. Journal of Neural Engineering, 4 (2007)Google Scholar
  5. 5.
    Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(3), 252–264 (1991)CrossRefGoogle Scholar
  6. 6.
    Asensio-Cubero, J., Gan, J.Q. Palaniappan, R.: Multiresolution analysis over simple graphs for brain computer interfaces. Journal of Neural Engineering, 10(4) (2013). doi: 10.1088/1741-2560/10/4/046014
  7. 7.
    Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (2006)Google Scholar
  8. 8.
    Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clinical Neurophysiology 110(11), 1842–1857 (1999)CrossRefGoogle Scholar
  9. 9.
    Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34, 483–519 (2013)CrossRefGoogle Scholar
  10. 10.
    Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist Non-dominated Sorting Genetic Algorithms for multi-objective optimisation: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Handl, J., Knowles, J.: Feature selection in unsupervised learning via multi-objective optimization. Intl. Journal of Computational Intelligence Research 2(3), 217–238 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Emmanouilidis, C., Hunter, A., MacIntyre, J.: A multi-objective evolutionary setting for feature selection and a commonality-based crossover operator. In: Proc. of the 2000 Congress on Evolutionary Computation, pp. 309–316. IEEE Press, New York (2000)Google Scholar
  13. 13.
    Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.: A methodology for feature selection using multi-objective genetic algorithms for handwritten digit string recognition. International Journal of Pattern Recognition and Artificial Intelligence 17(6), 903–929 (2003)CrossRefGoogle Scholar
  14. 14.
    Kim, Y., Street, W.N., Menczer, F.: Evolutionary model selection in unsupervised learning. Intelligent Data Analysis 6(6), 531–556 (2002)zbMATHGoogle Scholar
  15. 15.
    Morita, M., Sabourin, R., Bortolozzi, F., Suen, C.Y.: Unsupervised feature selection using multi-objective genetic algorithms for handwritten word recognition. In: Proc. of the Seventh International Conference on Document Analysis and Recognition, pp. 666–671. IEEE Press, New York (2003)Google Scholar
  16. 16.
    Gan, H., Sang, N., Huang, R., Tong, X., Dan, Z.: Using clustering analysis to improve semi-supervised classification. Neurocomputing 101, 290–298 (2013)CrossRefGoogle Scholar
  17. 17.
    Basu, S., Banerjee, A., Rooney, R.J.: Semi-supervised clustering by seeding. In: Proc. of the 19th International Conference on Machine Learning, pp. 11–18 (2003)Google Scholar
  18. 18.
    Dash, M., Liu, H.: Consistency-based search in feature selection. Artificial Intelligence 151, 155–176 (2003)zbMATHMathSciNetCrossRefGoogle Scholar
  19. 19.
    Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychological Meas. 20, 37–46 (1960)CrossRefGoogle Scholar
  20. 20.
    Ortega, J., Asensio-Cubero, J., Gan, J.Q., Ortiz, A.: Evolutionary multiobjective feature selection in multiresolution analysis for BCI. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2015, Part I. LNCS, vol. 9043, pp. 347–359. Springer, Heidelberg (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pedro Martín-Smith
    • 1
  • Julio Ortega
    • 1
    Email author
  • Javier Asensio-Cubero
    • 2
  • John Q. Gan
    • 2
  • Andrés Ortiz
    • 3
  1. 1.Department of Computer Architecture and Technology, CITICUniversity of GranadaGranadaSpain
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK
  3. 3.Department of Communications EngineeringUniversity of MalagaMalagaSpain

Personalised recommendations