Feature Selection of Motor Imagery EEG Signals Using Firefly Temporal Difference Q-Learning and Support Vector Machine

  • Saugat Bhattacharyya
  • Pratyusha Rakshit
  • Amit Konar
  • D. N. Tibarewala
  • Ramadoss Janarthanan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


Electroencephalograph (EEG) based Brain-computer Inter- face (BCI) research provides a non-muscular communication to drive assistive devices using movement related signals, generated from the motor activation areas of the brain. The dimensions of the feature vector play an important role in BCI research, which not only increases the computational time but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant features of a feature vector obtained from motor imagery EEG signals to improve their corresponding classification. In this paper we have proposed a feature selection method based on Firefly Algorithm and Temporal Difference Q-Learning. Here, we have applied our proposed method to the wavelet transform features of a standard BCI competition dataset. Support Vector Machines have been employed to determine the fitness function of the proposed method and obtain the resultant classification accuracy. We have shown that the accuracy of the reduced feature are considerably higher than the original features. This paper also demonstrates the superiority of the new method to its competitor algorithms.


Brain-Computer Interfacing Electroencephalography Firefly Algorithm Temporal Difference Q-Learning Support Vector Machines Wavelet Transforms 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tavella, M., Leeb, R., Rupp, R., Millan, J.R.: Towards Natural Non-invasive Hand Neuroprostheses for Daily Living. In: 32nd Annual Int. Conf. IEEE EMBS, pp. 126–129 (2010)Google Scholar
  2. 2.
    Muller-PutzGernot, R., Reinhold, S., Pfurtscheller, G., Neuper, C.: Temporal coding of brain patterns for direct limb control in humans. J. Fron. Neurosci. 4, 1–11 (2010)Google Scholar
  3. 3.
    Conradi, J., Blankertz, B., Tangermann, M., Kunzmann, V., Curio, G.: Brain-computer interfacing in tetraplegic patients with high spinal cord injury. Int. J. Bioelectromagnetism 11(2), 65–68 (2009)Google Scholar
  4. 4.
    Prasad, G., Herman, P., Coyle, D., McDonough, S., Crosbie, J.: Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J. Neuroeng. and Rehab. 7(1), 60–76 (2010)CrossRefGoogle Scholar
  5. 5.
    Vaughan, T.M., Heetderks, W.J., Trejo, L.J., Rymer, W.Z., Weinrich, M., Moore, M.M., Kubler, A., Dobkin, B.H., Birbaumer, N., Donchin, E., Wolpaw, E.W., Wolpaw, J.R.: Brain computer interface technology: A review of the second international meeting. IEEE Trans. Neural Syst. Rehab. Eng. 11(2), 94–109 (2003)CrossRefGoogle Scholar
  6. 6.
    Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Quatrano, L.A., Robinson, C.J., Vaughan, T.M.: Brain computer interface: a review of the first international meeting. IEEE Trans. Rehabilitation Eng. 8(2), 164–173 (2000)CrossRefGoogle Scholar
  7. 7.
    Anderson, R.A., Musallam, S., Pesaran, B.: Selecting the signals for a brain-machine interface. Curr. Opin. Neurobiol. 14(6), 720–726 (2004)CrossRefGoogle Scholar
  8. 8.
    Dornhege, G., Millan, J.R., Hinterberger, T., McFarland, D.J., Muller, K.R.: Toward Brain-Computer Interfacing. MIT Press, Massachusetts (2007)Google Scholar
  9. 9.
    Sanei, S., Chambers, J.A.: EEG Signal Processing. John Wiley & Sons, West Sussex (2007)Google Scholar
  10. 10.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2006)Google Scholar
  11. 11.
    Rakotomamonjy, A., Guigue, V., Mallet, G., Alvarado, V.: Ensemble of svms for improving brain computer interface. In: Int. Conf. on Artificial Neural Networks (2005)Google Scholar
  12. 12.
    Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for eeg-based brain-computer interfaces. J. Neural Eng. 4 (2007)Google Scholar
  13. 13.
    Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics 2, 433–459 (2010)CrossRefGoogle Scholar
  14. 14.
    Comon, P.: Independent component analysis: a new concept. Signal Processing 36(3), 287–314 (1994)CrossRefzbMATHGoogle Scholar
  15. 15.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)CrossRefGoogle Scholar
  16. 16.
    Hao, H., Liu, C.-L., Sako, H.: Comparison of Genetic Algortihm and Sequential Search Methods for Classifier Subset Selection. In: 7th Int. Conf Document Analysis & Recognition (ICDAR) (2003)Google Scholar
  17. 17.
    Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., Pfurtscheller, G.: Brain-computer communication: motivation, aim and impact of exploring a virtual apartment. IEEE Trans. Neural Sys. & Rehab. Engg. 15, 473–482 (2007)CrossRefGoogle Scholar
  18. 18.
    Tamraz, J.C., Comair, Y.G.: Atlas of regional anatomy of the brain using MRI with functional correlates. Springer (2006)Google Scholar
  19. 19.
    Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. J. Clin. Neurophysiology 110, 1842–1857 (1999)CrossRefGoogle Scholar
  20. 20.
    Darvishi, S., Al-Ani, A.: Brain-computer interface analysis using continuous wavelet transform and adaptive neuro-fuzzy classifier. In: 29th Int. Annu. Conf. IEEE Eng. Med. Biol. Soc., pp. 3220–3223 (2007)Google Scholar
  21. 21.
    Gaing, Z.L., Huang, H.S.: Wavelet Based Neural Network For Power Disturbance Classification. IEEE Trans. Power Delivery 19(4), 1560–1568 (2004)CrossRefGoogle Scholar
  22. 22.
    Kocaman, C., Ozdemir, M.: Comparison of Statistical Methods and Wavelet Energy Coefficients for Determining Two Common PQ Disturbances: Sag and Swell. In: Int. Conf. Electrical & Electronics Engg., ELECO 2009, pp. I-80–I-84 (2009)Google Scholar
  23. 23.
    Bhowmik, P., Rakshit, P., Konar, A., Nagar, A.K., Kim, E.: FA-TDQL: an adaptive memetic algorithm. In: Congress on Evolutionary Computation, pp. 1–8 (2012)Google Scholar
  24. 24.
    Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  25. 25.
    Mitchell, T.: Machine Learning. McGraw Hill (1997)Google Scholar
  26. 26.
    Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 38(1) (2008)Google Scholar
  27. 27.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  28. 28.
    Alpaydin, E.: Introduction to Machine Learning. MIT Press, Massachusetts (2009)Google Scholar
  29. 29.
    Neshatian, K., Zhang, M., Johnston, M.: Feature Construction and Dimension Reduction Using Genetic Programming. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 160–170. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  30. 30.
    Yang, S.X., Hu, Y., Meng, M.Q.H.: A Knowledge Based GA for Path Planning of Multiple Mobile Robots in Dynamic Environments. In: IEEE Conf. Robotics, Automation & Mechatronics, pp. 1–6 (2006)Google Scholar
  31. 31.
    Storn, R., Price, K.V.: Differential Evolutiona simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization 11(4), 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  32. 32.
    Chakraborty, J., Konar, A.: A Distributed Multi Robot Path Planning Using Particle Swarm Optimization. In: 2nd Nat. Conf. Recent Trends in Information Systems, pp. 216–221 (2008)Google Scholar
  33. 33.
    Bhattacharjee, P., Rakshit, P., Goswami, I., Konar, A., Nagar, A.K.: Multi-robot path-planning using artificial bee colony optimization algorithm. In: Third World Congress on Nature & Biologically Inspired Computing, pp. 219–224 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Saugat Bhattacharyya
    • 1
    • 4
  • Pratyusha Rakshit
    • 1
  • Amit Konar
    • 1
  • D. N. Tibarewala
    • 2
    • 4
  • Ramadoss Janarthanan
    • 3
    • 5
  1. 1.Dept. of Electronics and Telecommunication Engg.Jadavpur UniversityKolkataIndia
  2. 2.School of Bioscience and Engg.Jadavpur UniversityKolkataIndia
  3. 3.Computer Science Dept.Jadavpur UniversityKolkataIndia
  4. 4.Jadavpur UniversityKolkataIndia
  5. 5.TJS Engineering CollegeChennaiIndia

Personalised recommendations