Skip to main content
SpringerLink
Log in

Impact of Feature Selection on EEG Based Motor Imagery

  • Conference paper
  • First Online:
Information and Communication Technology for Competitive Strategies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 40))

Abstract

An EEG based motor imagery translates the motor intention of any subject into control signal by classifying EEG data of different imagination tasks such as hand and feet movements. As indicated by study, it is found that there are almost around 1 in 50 individuals living with loss of motion roughly 5.4 million individuals. For this sort of inability, EEG based BCI for motor imagery of right hand and feet movement imagination is acquired and classified. Short time Fourier transform and wavelet features are extracted and classified with and without feature selection. Ranking method is used for feature selection. Both classification outcomes are comparatively analyzed and observed that there is an increment in classification accuracy when features are classified after feature selection.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. Proc. IEEE 89(7), 1123–1134 (2001)

    Article  Google Scholar 

  2. Ang, K.K., Guan, C., Chua, K.S. G., Ang, B.T., Kuah, C., Wang, C., Zhang, H.: Clinical study of neuro rehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. In: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pp. 5549–5552. IEEE, Aug 2010

    Google Scholar 

  3. Hanakawa, T., Immisch, I., Toma, K., Dimyan, M.A., Van Gelderen, P., Hallett, M.: Functional properties of brain areas associated with motor execution and imagery. J. Neurophysiol. 89(2), 989–1002 (2003)

    Article  Google Scholar 

  4. Arvaneh, M., Guan, C., Ang, K.K., Quek, C.: Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Trans. Biomed. Eng. 58(6), 1865–1873 (2011)

    Article  Google Scholar 

  5. Brunner, C., Naeem, M., Leeb, R., Graimann, B., Pfurtscheller, G.: Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recogn. Lett. 28(8), 957–964 (2007)

    Article  Google Scholar 

  6. Townsend, G., Graimann, B., Pfurtscheller, G.: Continuous EEG classification during motor imagery-simulation of an asynchronous BCI. IEEE Trans. Neural. Syst. Rehabil. Eng. 12(2), 258–265 (2004)

    Article  Google Scholar 

  7. Qin, L., He, B.: A wavelet-based time-frequency analysis approach for classification of motor imagery for brain computer interface applications. J. Neural. Eng. 2(4), 65 (2005)

    Article  MathSciNet  Google Scholar 

  8. Wang, Y., Gao, S., Gao, X.: Common spatial pattern method for channel selelction in motor imagery based brain-computer interface. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the (pp. 5392–5395). IEEE, Jan 2006

    Google Scholar 

  9. Herman, P., Prasad, G., McGinnity, T.M., Coyle, D.: Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Trans. Neural. Syst. Rehabil. Eng. 16(4), 317–326 (2008)

    Article  Google Scholar 

  10. Bhattacharyya, S., Sengupta, A., Chakraborti, T., Konar, A., Tibarewala, D.N.: Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata. Med. Biol. Eng. Comput. 52(2), 131–139 (2014)

    Article  Google Scholar 

  11. Steyrl, D., Scherer, R., Frstner, O., Mller-Putz, G.R.: Motor imagery brain-computer interfaces: random forests vs regularized LDAnon-linear beats linear. In Proceedings of the 6th International Brain-Computer Interface Conference, Sept 2014

    Google Scholar 

  12. Canal, M.R.: Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals. J. Med. Syst. 34(1), 91–94 (2010)

    Article  MathSciNet  Google Scholar 

  13. Gorur, D., Halici, U., Aydin, H., Ongun, G., Ozgen, F., Leblebicioglu, K.: Sleep spindles detection using short time Fourier transform and neural networks. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN’02, vol. 2, pp. 1631–1636. IEEE (2002)

    Google Scholar 

  14. Almeida, L.B.: The fractional Fourier transform and time-frequency representations. IEEE Trans. Signal Process. 42(11), 3084–3091 (1994)

    Article  Google Scholar 

  15. Akin, M.: Comparison of wavelet transform and FFT methods in the analysis of EEG signals. J. Med. Syst. 26(3), 241–247 (2002)

    Article  Google Scholar 

  16. Cvetkovic, D., Beyli, E.D., Cosic, I.: Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: a pilot study. Digit. Signal Proc. 18(5), 861–874 (2008)

    Article  Google Scholar 

  17. Soltani, S.: On the use of the wavelet decomposition for time series prediction. Neurocomputing 48(1), 267–277 (2002)

    Article  MathSciNet  Google Scholar 

  18. Faust, O., Acharya, U.R., Adeli, H., Adeli, A.: Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 26, 56–64 (2015)

    Article  Google Scholar 

  19. Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17(4), 491–502 (2005)

    Article  Google Scholar 

  20. Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowl. Data Eng. 15(6), 1437–1447 (2003)

    Article  Google Scholar 

  21. Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5(Oct) 1205–1224 (2004)

    Google Scholar 

  22. https://machinelearningmastery.com/perform-feature-selection-machine-learning-data-weka/

  23. http://bnci-horizon-2020.eu/database/data-sets

Download references

Acknowledgements

This work was supported by a grant from the National Institute of Technology, Raipur. The authors acknowledge the Head of Department of Information Technology, National Institute of Technology, Raipur. The authors also acknowledge the constructive criticisms by several anonymous reviewers of an earlier draft of this paper.

Author information

Authors and Affiliations

  1. Department of IT, National Institute of Technology, Raipur, Raipur, Chhattisgarh, India

    Mridu Sahu & Sneha Shukla

Authors
  1. Mridu Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sneha Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mridu Sahu .

Editor information

Editors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Macau, China

    Simon Fong

  2. Department of Electronics and Communication Engineering, ITM University, Gwalior, India

    Shyam Akashe

  3. Smt. Kashibai Navale College of Engineering, Pune, India

    Parikshit N. Mahalle

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sahu, M., Shukla, S. (2019). Impact of Feature Selection on EEG Based Motor Imagery. In: Fong, S., Akashe, S., Mahalle, P. (eds) Information and Communication Technology for Competitive Strategies. Lecture Notes in Networks and Systems, vol 40. Springer, Singapore. https://doi.org/10.1007/978-981-13-0586-3_73

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-0586-3_73

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0585-6

  • Online ISBN: 978-981-13-0586-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation