Skip to main content

A New Way of Channel Selection in the Motor Imagery Classification for BCI Applications

  • Conference paper
  • First Online:
Health Information Science (HIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11148))

Included in the following conference series:

Abstract

Nowadays, motor imagery classification in electroencephalography (EEG) based brain computer interface (BCI) systems is a very important research topic in the study of brain science. As EEG contains multi-channel EEG recordings with huge amount of data, it is sometimes very challenging to extract more representative information from original EEG data for efficient classification of motor imagery (MI) tasks. Thus, it is necessary to diminish the redundant information from the original EEG signal selecting appropriate channels and also to reduce computational cost. Addressing this problem, we intend to develop a methodology based on channel selection for classification of MI tasks in the BCI applications. In this study, we introduce a new way of channel selection considering anatomical and functional structural of the human brain and also investigate its impact in the classification performance. In this proposed method, at first we select the channels from motor cortex area, and then decompose EEG signals using wavelet energy function into several bands of real and imaginary coefficients. The relevant band’s coefficient energy has been used as feature vector in this research. After that, the extracted features are tested by three popular machine learning method: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). The method is evaluated on a benchmark dataset IVa (BCI competition III) and the results demonstrate classification improvement with less computational cost over the existing methods.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Kamousi, B., Liu, Z., He, B.: Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles analysis. IEEE Trans. Neural Syst. Rehabil. Eng. 13(2), 166–171 (2005)

    Article  Google Scholar 

  2. Wolpaw, J.R., et al.: Brain–computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)

    Article  Google Scholar 

  3. Blankertz, B., et al.: The Berlin brain–computer interface: accurate performance from first-session in BCI-naive subjects. IEEE Trans. Biomed. Eng. 55(10), 2452–2462 (2008)

    Article  Google Scholar 

  4. Pfurtscheller, G., et al.: EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr. Clin. Neurophysiol. 103(6), 642–651 (1997)

    Article  Google Scholar 

  5. Popescu, F., et al.: Single trial classification of motor imagination using 6 dry EEG electrodes. PLoS ONE 2(7), e637 (2007)

    Article  Google Scholar 

  6. Lim, C.-K.A., Chia, W.C.: Analysis of single-electrode EEG rhythms using MATLAB to elicit correlation with cognitive stress. Int. J. Comput. Theory Eng. 7(2), 149 (2015)

    Article  Google Scholar 

  7. Phinyomark, A., Chusak, L., Pornchai, P.: Optimal wavelet functions in wavelet denoising for multifunction myoelectric control. ECTI Trans. Electr. Eng. Electron. Commun. 8(1), 43–52 (2010)

    Google Scholar 

  8. Oh, S.-H., Lee, Y.-R., Kim, H.-N.: A novel EEG feature extraction method using Hjorth parameter. Int. J. Electron. Electr. Eng. 2(2), 106–110 (2014)

    Article  Google Scholar 

  9. Resalat, S.N., Saba, V.: A study of various feature extraction methods on a motor imagery based brain computer interface system. Basic Clin. Neurosci. 7(1), 13–20 (2016)

    Google Scholar 

  10. Yang, B.-H., et al.: Feature extraction for EEG-based brain–computer interfaces by wavelet packet best basis decomposition. J. Neural Eng. 3(4), 251 (2006)

    Article  Google Scholar 

  11. Shan, H., et al.: EEG-based motor imagery classification accuracy improves with gradually increased channel number. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE (2012)

    Google Scholar 

  12. Blankertz, B., et al.: The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Trans. Biomed. Eng. 51(6), 1044–1051 (2004)

    Article  Google Scholar 

  13. Siuly, S., Li, Y., Wen, P.: Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain computer interface. Comput. Methods Programs Biomed. 113(3), 767–780 (2014)

    Article  Google Scholar 

  14. Siuly, S., Li, Y., Wen, P.: Comparisons between motor area EEG and all-channels EEG for two algorithms in motor imagery task classification. Biomed. Eng. Appl. Basis Commun. (BME) 26(3), 1450040 (2014). 10 pages

    Article  Google Scholar 

  15. Siuly, Y.L., Wen, P.: Identification of motor imagery tasks through CC-LR algorithm in brain computer interface. Int. J. Bioinform. Res. Appl. 9(2), 156–172 (2013)

    Article  Google Scholar 

  16. Pfurtscheller, G., et al.: Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31(1), 153–159 (2006)

    Article  Google Scholar 

  17. Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4), 411–430 (2000)

    Article  Google Scholar 

  18. Erfani, A., Abbas, E.: The effects of mental practice and concentration skills on EEG brain dynamics during motor imagery using independent component analysis. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS 2004), vol. 1. IEEE (2004)

    Google Scholar 

  19. Wang, S., James, C.J.: Extracting rhythmic brain activity for brain-computer interfacing through constrained independent component analysis. Comput. Intell. Neurosci. 2007, 9 (2007). Article ID 41468

    Article  Google Scholar 

  20. Ting, W., et al.: EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 41(6), 618–625 (2008)

    Article  Google Scholar 

  21. Lotte, F., Guan, C.: Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58(2), 355–362 (2011)

    Article  Google Scholar 

  22. Baziyad, A.G., Ridha, D.: A study and performance analysis of three paradigms of wavelet coefficients combinations in three-class motor imagery based BCI. In: 2014 5th International Conference on Intelligent Systems, Modelling and Simulation. IEEE (2014)

    Google Scholar 

  23. Siuly, S., Li, Y., Zhang, Y.: EEG Signal Analysis and Classification: Techniques and Applications. Health Information Science. Springer, New York (2016). https://doi.org/10.1007/978-3-319-47653-7

    Book  Google Scholar 

  24. Zarei, R., He, J., Siuly, S., Zhang, Y.: A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals. Comput. Methods Programs Biomed. 146, 47–57 (2017)

    Article  Google Scholar 

  25. Siuly, S., Li, Y.: Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 20(4), 526–538 (2012)

    Article  Google Scholar 

  26. Siuly, S., Li, Y.: Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach. Neural Comput. Appl. 26(4), 799–811 (2014)

    Article  Google Scholar 

  27. Kabir, E., Siuly, S., Zhang, Y.: Epileptic seizure detection from EEG signals using logistic model trees. Brain Inform. 3(2), 93–100 (2016)

    Article  Google Scholar 

  28. Siuly, S., Kabir, E., Wang, H., Zhang, Y.: Exploring sampling in the detection of multicategory EEG signals. Comput. Math. Methods Med. 2015, 1–12 (2015). Article ID 576437

    Article  Google Scholar 

  29. Kabir, E., Siuly, S., Cao, J., Wang, H.: A computer aided analysis scheme for detecting epileptic seizure from EEG data. Int. J. Comput. Intell. Syst. 11(1), 663–671 (2018)

    Article  Google Scholar 

  30. Siuly, S., Li, Y.: Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification. Comput. Methods Programs Biomed. 119(1), 29–42 (2015)

    Article  Google Scholar 

  31. Siuly, S., Yin, X., Hadjiloucas, S., Zhang, Y.: Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers. Comput. Methods Programs Biomed. 127, 64–82 (2016)

    Article  Google Scholar 

  32. Supriya, S., Siuly, S., Zhang, Y.: Automatic epilepsy detection from EEG introducing a new edge weight method in the complex network. Electron. Lett. 52(17), 1430–1432 (2016)

    Article  Google Scholar 

  33. Siuly, S., Wang, H., Zhuo, G., Zhang, Y.: Analyzing EEG signal data for detection of epileptic seizure: introducing weight on visibility graph with complex network feature. In: ADC 2016: Databases Theory and Applications, pp. 56–66

    Google Scholar 

Download references

Acknowledgment

The authors thank Fraunhofer FIRST, Intelligent Data Analysis Group (Klaus-Robert Müller, Benjamin Blankertz), and Campus Benjamin Franklin of the Charité - University Medicine Berlin, Department of Neurology, Neurophysics Group (Gabriel Curio) for providing the data set.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siuly Siuly .

Editor information

Editors and Affiliations

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

Joadder, M.A.M., Siuly, S., Kabir, E. (2018). A New Way of Channel Selection in the Motor Imagery Classification for BCI Applications. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-01078-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01077-5

  • Online ISBN: 978-3-030-01078-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics