Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Intelligent Human Computer Interaction (IHCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11278))

Included in the following conference series:

  • 898 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  12. Nitschke, M.F., Kleinschmidt, A., Wessel, K., Frahm, J.: Somatotopic motor representation in the human anterior cerebellum. Brain 119, 1023–1029 (1996)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  17. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2012)

    MATH  Google Scholar 

  18. He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, vol. 16, pp. 153–160 (2004)

    Google Scholar 

  19. Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8(May), 1027–1061 (2007)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  21. Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1970)

    Article  Google Scholar 

  22. Schlögl, A.: The electroencephalogram and the adaptive autoregressive model: theory and applications. Shaker, Aachen (2000)

    Google Scholar 

  23. Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2014)

    MATH  Google Scholar 

  24. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2004)

    Google Scholar 

  25. Christopher, M.B.: Pattern Recognition and Machine Learning. Springer, New York (2016)

    Google Scholar 

Download references

Acknowledgments

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.

Author information

Authors and Affiliations

  1. School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, India

    Batala Sandhya & Manjunatha Mahadevappa

Authors
  1. Batala Sandhya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manjunatha Mahadevappa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Batala Sandhya .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sandhya, B., Mahadevappa, M. (2018). EEG-Based Detection of Brisk Walking Motor Imagery Using Feature Transformation Techniques. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04021-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04020-8

  • Online ISBN: 978-3-030-04021-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation