EEG-Based Detection of Brisk Walking Motor Imagery Using Feature Transformation Techniques

  • Batala SandhyaEmail author
  • Manjunatha Mahadevappa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11278)


Recently motor imagery (MI) based Brain-Computer Interface (BCI) for lower limb rehabilitation is gaining attention. Feature extraction and dimensionality reduction are crucial signal processing blocks that determine the performance of a BCI system. In this work, various features, that are, band power (BP) features, autoregressive (AAR) parameters and Hjorth (HJ) parameters, widely used in BCI research are studied for their efficacy in discriminating MI brisk walking activity from the idle state. Feature transformation (FT) techniques, a type of dimensionality reduction techniques, namely Principal Component Analysis (PCA), Locality Preserving Projections (LPP) and Local Fisher Discriminant analysis (LFDA) are then applied on the extracted features to map them into a lower dimensional subspace. Ten-fold cross-validation is used to choose the dimension of the projection subspace. In a group of five novice users, it is observed that none of these features separately or all taken together represented the activity well. On using FT techniques, the discriminability of the fused features improved. Among the three techniques, LFDA performed the best showing an average increase in classification accuracy (26.9%), sensitivity (37.6%) and specificity (26.2%) over the average values obtained when no FT technique are used for the group of five subjects.


EEG BCI Motor imagery Feature transformation 



We would like to thank all the subjects who participated in this study. We are thankful to Professor N.K. Kishore of IIT Kharagpur for valuable discussions and authorities of IIT Kharagpur for encouragement in the work and permission to publish the paper.


  1. 1.
    Wolpaw, J.R., McFarland, D.J.: Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl. Acad. Sci. U.S.A. 101(51), 17849–17854 (2004)CrossRefGoogle Scholar
  2. 2.
    Townsend, G., et al.: A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin. Neurophysiol. 121(7), 1109–1120 (2010)CrossRefGoogle Scholar
  3. 3.
    Leeb, R., Friedman, D., Müller-Putz, G.R., Scherer, R., Slater, M., Pfurtscheller, G.: Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput. Intell. Neurosci. 2007, 7 (2007)CrossRefGoogle Scholar
  4. 4.
    Müller-Putz, G.R., Scherer, R., Pfurtscheller, G., Rupp, R.: EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci. Lett. 382(1–2), 169–174 (2005)CrossRefGoogle Scholar
  5. 5.
    Pfurtscheller, G., Müller, G.R., Pfurtscheller, J., Gerner, H.J., Rupp, R.: ‘Thought’–control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351(1), 33–36 (2003)CrossRefGoogle Scholar
  6. 6.
    Bashashati, A., Fatourechi, M., Ward, R.K., Birch, G.E.: A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals. J. Neural Eng. 4(2), R32 (2007)CrossRefGoogle Scholar
  7. 7.
    Machado, S., et al.: EEG-based brain-computer interfaces: an overview of basic concepts and clinical applications in neurorehabilitation. Rev. Neurosci. 21(6), 451–468 (2010)CrossRefGoogle Scholar
  8. 8.
    Dickstein, R., Dunsky, A., Marcovitz, E.: Motor imagery for gait rehabilitation in post-stroke hemiparesis. Phys. Ther. 84(12), 1167–1177 (2004)Google Scholar
  9. 9.
    Wang, P.T., King, C.E., Chui, L.A., Do, A.H., Nenadic, Z.: Self-paced brain–computer interface control of ambulation in a virtual reality environment. J. Neural Eng. 9(5), 056016 (2012)CrossRefGoogle Scholar
  10. 10.
    Hashimoto, Y., Ushiba, J.: EEG-based classification of imaginary left and right foot movements using beta rebound. Clin. Neurophysiol. 124(11), 2153–2160 (2013)CrossRefGoogle Scholar
  11. 11.
    Yang, H., Guan, C., Wang, C.C., Ang, K.K.: Detection of motor imagery of brisk walking from electroencephalogram. J. Neurosci. Methods 244, 33–44 (2015)CrossRefGoogle Scholar
  12. 12.
    Nitschke, M.F., Kleinschmidt, A., Wessel, K., Frahm, J.: Somatotopic motor representation in the human anterior cerebellum. Brain 119, 1023–1029 (1996)CrossRefGoogle Scholar
  13. 13.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4(2), R1 (2007)CrossRefGoogle Scholar
  14. 14.
    Vidaurre, C., Krämer, N., Blankertz, B., Schlögl, A.: Time domain parameters as a feature for EEG-based brain–computer interfaces. Neural Netw. 22(9), 1313–1319 (2009)CrossRefGoogle Scholar
  15. 15.
    Van Der Maaten, L., Postma, E., Van den Herik, J.: Dimensionality reduction: a comparative. J. Mach. Learn. Res. 10, 66–71 (2009)Google Scholar
  16. 16.
    García-Laencina, P.J., Rodríguez-Bermudez, G., Roca-Dorda, J.: Exploring dimensionality reduction of EEG features in motor imagery task classification. Expert Syst. Appl. 41(11), 5285–5295 (2014)CrossRefGoogle Scholar
  17. 17.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)zbMATHGoogle Scholar
  18. 18.
    He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, vol. 16, pp. 153–160 (2004)Google Scholar
  19. 19.
    Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8(May), 1027–1061 (2007)zbMATHGoogle Scholar
  20. 20.
    Neuper, C., Müller-Putz, G.R., Scherer, R., Pfurtscheller, G.: Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog. Brain Res. 159, 393–4099 (2006)CrossRefGoogle Scholar
  21. 21.
    Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1970)CrossRefGoogle Scholar
  22. 22.
    Schlögl, A.: The electroencephalogram and the adaptive autoregressive model: theory and applications. Shaker, Aachen (2000)Google Scholar
  23. 23.
    Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2014)zbMATHGoogle Scholar
  24. 24.
    Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2004)Google Scholar
  25. 25.
    Christopher, M.B.: Pattern Recognition and Machine Learning. Springer, New York (2016)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Medical Science and TechnologyIndian Institute of Technology KharagpurKharagpurIndia

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