The Impact of PSO Based Dimension Reduction on EEG Classification

  • Adham Atyabi
  • Martin H. Luerssen
  • Sean P. Fitzgibbon
  • David M. W. Powers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7670)

Abstract

The high dimensional nature of EEG data due to large electrode numbers and long task periods is one of the main challenges of studying EEG. Evolutionary alternatives to conventional dimension reduction methods exhibit the advantage of not requiring the entire recording sessions for operation. Particle Swarm Optimization (PSO) is an Evolutionary method that achieves performance through evaluation of several generations of possible solutions. This study investigates the feasibility of a 2 layer PSO structure for synchronous reduction of both electrode and task period dimensions using 4 motor imagery EEG data. The results indicate the potential of the proposed PSO paradigm for dimension reduction with insignificant losses in classification and the practical uses in subject transfer applications.

Keywords

Particle Swarm Optimization Electroencephalogram Brain Computer Interface 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Atyabi, A., Luerssen, M., Fitzgibbon, S.P., Powers, D.M.W.: Dimension Reduction in EEG Data using Particle Swarm Optimization. In: IEEE Congress on computational Intelligence, CEC 2012 (2012)Google Scholar
  2. 2.
    Tov, E.Y., Inbar, G.F.: Feature selection for the classification of movements from single movement-related potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10(3), 170–177 (2002)CrossRefGoogle Scholar
  3. 3.
    Dias, N.S., Jacinto, L.R., Mendes, P.M., Correia, J.H.: Feature Down Selection in Brain Computer Interface. In: Proceeding of the 4th International IEEE EMBS Conference on Neural Engineering, pp. 323–326 (2009)Google Scholar
  4. 4.
    Largo, R., Munteanu, C., Rosa, A.: CAP Event Detection by Wavelets and GA Tuning. In: WISP 2005, pp. 44–48 (2005)Google Scholar
  5. 5.
    Zhang, X., Wang, X.: A genetic algorithm based time-Frequency Approach to a Movement Prediction task. In: Proceeding of the 7th World Congress on Intelligent Control and Automation, pp. 1032–1036 (2008)Google Scholar
  6. 6.
    Palaniappan, R., Raveendran, P.: Genetic Algorithm to select features for Fuzzy ARTMAP classification of evoked EEG, pp. 53-56 (2002)Google Scholar
  7. 7.
    Jin, J., Wang, X., Zhang, J.: Optimal Selection of EEG Electrodes via DPSO Algorithm. In: Proceeding of the 7th World Congress on Intelligent Control and Automation, pp. 5095–5099 (2008)Google Scholar
  8. 8.
    Hasan, B.A.S., Gan, J.Q., Zhang, Q.: Multi-Objective Evolutionary Methods for channel selection in brain Computer interface: Some Preliminary Experimental Results. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–6 (2010)Google Scholar
  9. 9.
    Hasan, B.A.S., Gan, J.Q.: Multi-Objective Particle Swarm Optimization for Channel Selection in Brain Computer Interface. In: The UK Workshop on Computational Intelligence (UKCI 2009), Nottingham, UK (2009)Google Scholar
  10. 10.
    Moubayed, N.A., Hasan, B.A.S., Gan, J.Q., Petrovski, A., McCall, J.: Binary-SDMOPSO and its application in channel selection for brain computer interfaces. In: 2010 UK Workshop on Computational Intelligence (UKCI), pp. 1–6 (2010)Google Scholar
  11. 11.
    Khushaba, R.N., Al-Ani, A., Al-Jumaily, A., Nguyen, H.T.: A Hybrid Nonlinear-Discriminant Analysis Feature Projection Technique. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 544–550. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Atyabi, A., Luerssen, M., Fitzgibbon, S.P., Powers, D.M.W.: Evolutionary feature selection and electrode reduction for EEG classification. In: IEEE Congress on Computational Intelligence, CEC 2012 (2012)Google Scholar
  13. 13.
    Atyabi, A., Luerssen, M., Fitzgibbon, S.P., Powers, D.M.W.: Adapting Subject-Independent Task-Specific EEG Feature Masks using PSO. In: IEEE Congress on computational Intelligence, CEC 2012 (2012)Google Scholar
  14. 14.
    Hwang, Y.K., Chen, P.C.: A Heuristic and Complete Planner for the Classical Mover’s Problem. In: Proceedings of the 1995 IEEE International Conference on Robotics and Automation, pp. 729–736. IEEE (1995)Google Scholar
  15. 15.
    Atyabi, A., Fitzgibbon, S.P., Powers, D.M.W.: Multiplying the Mileage of Your Dataset with Subwindowing. In: Hu, B., Liu, J., Chen, L., Zhong, N. (eds.) BI 2011. LNCS, vol. 6889, pp. 173–184. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Atyabi, A., Fitzgibbon, S.P., Powers, D.M.W.: Biasing the Overlapping and Non-Overlapping Sub-Windows of EEG recording. In: IEEE International Joint Conference on Neural Networks, IJCNN 2012 (2012)Google Scholar
  17. 17.
    Atyabi, A., Powers, D.M.W.: The impact of Segmentation and Replication on Non-Overlapping windows: An EEG study. In: The Second International Conference on Information Science and Technology, ICIST 2012, China (2012)Google Scholar
  18. 18.
    Blankertz, B., Müller, K.-R., Krusienski, D.J., Schalk, G., Wolpaw, J.R., Schlögl, A., Pfurtscheller, G., del R. Millán, J., Schröder, M., Birbaumer, N.: The BCI competition III:Validating alternative approaches to actual BCI problems. Neural Syst. Rehabil. Eng. 14(2), 153–159 (2006)Google Scholar
  19. 19.
    Powers, D.M.W.: Recall and Precision versus the Bookmaker. In: International Conference on Cognitive Science (ICSC 2003), pp. 529–534 (2003)Google Scholar
  20. 20.
    Powers, D.M.W.: Evaluation: From Precision, Recall and F-Measure to ROC. Informedness, Markedness & Correlation. Journal of Machine Learning Technologies 2(1), 37–63 (2011)MathSciNetGoogle Scholar
  21. 21.
    Powers, D.M.W.: The Problem of Kappa. In: 13th Conference of the European Chapter of the Association for Computational Linguistics, Avignon France (April 2012)Google Scholar
  22. 22.
    Chih-Chung, C., Chih-Jen, L.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3), 27:1–27:27 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Adham Atyabi
    • 1
  • Martin H. Luerssen
    • 1
  • Sean P. Fitzgibbon
    • 1
  • David M. W. Powers
    • 1
    • 2
  1. 1.School of Computer Science, Engineering and Mathematics (CSEM)Flinders UniversityAustralia
  2. 2.Beijing Municipal Lab for Multimedia & Intelligent SoftwareBeijing University of TechnologyBeijingChina

Personalised recommendations