Skip to main content
SpringerLink
Log in

EEG Classification for MI-BCI with Independent Component Analysis

  • Conference paper
  • First Online:
Book cover Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 (CORES 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

Included in the following conference series:

Abstract

Independent Component Analysis (ICA) is often used in EEG signal processing but only when the matrix of channels is big enough. If the matrix is small, the artifacts usually cannot be extracted as single components and the decision which components should be removed from the components’ set is impeded. Therefore, in our opinion, in order to apply ICA successfully for a low-dimensional EEG, the strategy for dealing with the components’ set should be reversed.

In the paper we propose a strategy of searching for components correlating with the desired brain activity, instead of looking for artifact-components. Obviously, since the brain activity depends on the task at hand, different tasks would require adaptation of the proposed approach but the overall scheme is independent from the task. In the paper we describe the strategy and illustrate it via the experiment with a simple 2-states Motor Imagery Brain Computer Interface. Our results show that even if we added only one spare channel to the core set of two channels (C3 and C4) essential for hand movement recognition, we obtained 18% increase in the recognition accuracy after applying ICA and our strategy.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, Y., Shangkai, G., Xiaorong, G.: Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (2005)

    Google Scholar 

  2. Oja, E., Yuan, Z.: The FastICA algorithm revisited: convergence analysis. IEEE Trans. Neural Netw. 17(6), 1370–1381 (2006)

    Article  Google Scholar 

  3. Augustyniak, P.: Adaptive wavelet discrimination of muscular noise in the ECG. In: Proceedings of Computers in Cardiology (IEEE-EMB), vol. 33, pp. 481–484 (2006)

    Google Scholar 

  4. Rejer, I., Górski, P.: Independent component analysis for EEG data preprocessing - algorithms comparison. In: Computer Information Systems and Industrial Management. LNCS, vol. 8104, pp. 108–119. Springer, Heidelberg (2013)

    Google Scholar 

  5. Yang, R., Song, A., Xu, B.: Feature extraction of motor imagery EEG based on wavelet transform and higher-order statistics. Int. J. Wavelets Multiresolut. Inf. Process. 8(3), 373–384 (2010)

    Article  MATH  Google Scholar 

  6. Müller-Putz, G.R., Kaiser, V., Solis-Escalante, T., Pfurtscheller, G.: Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG. Med. Biol. Eng. Comput. 48(3), 229–233 (2010)

    Google Scholar 

  7. Pfurtscheller, G., Neuper, Ch., Schlögl, A., Lugger, K.: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehabil. Eng. 6(3), 316–325 (1998)

    Google Scholar 

  8. Burduk, R.: Imprecise information in Bayes classifier. Pattern Anal. Appl. 15(2), 147–153 (2012)

    Article  MathSciNet  Google Scholar 

  9. Vigario, R.N.: Extraction of ocular artefacts from EEG using independent component analysis. Electroencephalogr. Clin. Neurophysiology 103(3), 395–404 (1997)

    Article  Google Scholar 

  10. Wallstrom, G.L., Kass, R.E., Miller, A., Cohn, J.F., Fox, N.A.: Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. Int. J. Psychophysiol. 53(2), 105–119 (2004)

    Article  Google Scholar 

  11. Jung, T.P., Humphries, C., Lee, T.W., Makeig, S., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Extended ICA removes artifacts from electroencephalographic recordings. Advances in Neural Information Processing Systems, pp. 894–900 (1998)

    Google Scholar 

  12. Zhou, W., Gotman, J.: Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS 2004, vol. 1, pp. 392–395 (2004)

    Google Scholar 

  13. Xue, Z., Li, J., Li, S., Wan, B.: Using ICA to remove eye blink and power line artifacts in EEG. In: Innovative Computing, Information and Control, vol. 3, pp. 107–110 (2006)

    Google Scholar 

  14. Delorme, A., Palmer, J., Onton, J., Oostenveld, R., Makeig, S.: Independent EEG sources are dipolar. PloS One 7(2), e30135 (2012)

    Article  Google Scholar 

  15. Jasper, H.H.: The ten-twenty electrode system of the international federation in electroencephalography and clinical neurophysiology. EEG J. 10, 371–375 (1958)

    Google Scholar 

  16. Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lécuyer, A.: OpenViBE: an open-source software platform to design, test and use brain-computer interfaces in real and virtual environments. Presence Teleoperators Virtual Environ. 19(1) (2010)

    Google Scholar 

  17. Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 252–264 (1991)

    Article  Google Scholar 

  18. Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. Ser. B (Methodological) 58(1), 267–288 (1996)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

    Izabela Rejer & Paweł Górski

Authors
  1. Izabela Rejer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paweł Górski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Izabela Rejer .

Editor information

Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Rejer, I., Górski, P. (2018). EEG Classification for MI-BCI with Independent Component Analysis. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59162-9_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59161-2

  • Online ISBN: 978-3-319-59162-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation