Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 10119–10132 | Cite as

Algorithms benchmarking for removing EOG artifacts in brain computer interface

  • Wen QiEmail author
Article
First Online:

Abstract

In this paper, the author investigates and compares two types of algorithms for removing EOG artifacts in EEG measurements for brain computer interface applications. First, the author describes the sources that introduce EOG artifacts and briefly reviews the existing algorithms for EOG artifacts removal in EEG signals. Two selected algorithms—recursive least square (RLS) and blind source separation (BSS) are discussed in details. The algorithms were chosen either for its good performance or for its low computational charge to be used in ambulatory monitoring devices that are developed within our research program. In the third part, the experimental protocol to collect EOG and EEG measurements are described. The forth part summarizes the results after applying two algorithms to the collected EEG signals. From these results, we draw conclusions on the performance of these selected algorithms. Several possible improvements or challenges are proposed in the discussion.

Keywords

EOG artefacts EEG analysis Digital signal processing Recursive least square (RLS) Blind source separation (BSS) Brain computer interface 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

The authors would like to thank the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (No. TP2015029) for financial support.

References

  1. 1.
    Atwood, H.L., MacKay, W.A.: Essentials of Neurophysiology, pp. 200–205. B.C. Decker, Hamilton (1993)Google Scholar
  2. 2.
    Tyner, F.S., Knott, J.R.: Fundamentals of EEG Technology, Volume 1: Basic Concepts and Methods. Raven press, New York (1989)Google Scholar
  3. 3.
    Niedermeyer, E., Lopes da Silva, F.H.: Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 3rd edn, pp. 351–354. Lippincott, Williams & Wilkins, Philadelphia (1993)Google Scholar
  4. 4.
    Anderer, P., Roberts, S., Schlogl, A., Gruber, G., Klosch, G., Herrmann, W., et al.: Artifact processing in computerized analysis of sleep EEG a review. Neuropsychobiology 40, 150–157 (1999)CrossRefGoogle Scholar
  5. 5.
    McFarland, D.J., McCane, L.M., David, S.V., Wolpaw, J.R.: Spatial filter selection for EEG-based communication. Electroencephalogr. Clin. Neurophysiol. 103, 386–394 (1997)CrossRefGoogle Scholar
  6. 6.
    Goncharova II, D.J., McFarland, T.M., Vaughan, J.R.: Wolpaw, EMG contamination of EEG: spectral and topographical characteristics. Clin. Neurophysiol. 114, 1580–1593 (2003)CrossRefGoogle Scholar
  7. 7.
    McFarland, D.J., Sarnacki, W.A., Vaughan, T.M., Wolpaw, J.R.: Braincomputer interface (BCI) operation: signal and noise during early training sessions. Clin. Neurophysiol. 116, 56–62 (2005)CrossRefGoogle Scholar
  8. 8.
    Gratton, G., Coles, M.G.H., Donchin, E.: A new method for the online removal of ocular artifact. Electroencephalogr. Clin. Neurophysiol. 55, 468–484 (1983)CrossRefGoogle Scholar
  9. 9.
    Kenemans, J.L., Molenaar, P.C.M., Verbaten, M.N., Slangen, J.L.: Removal of the ocular artifact from the EEG: a comparison of time and frequency domain methods with simulated and real data. Psychophysiology 28, 115–121 (1991)CrossRefGoogle Scholar
  10. 10.
    Whitton, J.L., Lue, F., Moldofsky, H.: A spectral method for removing eye movement artifacts from the EEG. Electroencephalogr. Clin. Neurophysiol. 44, 735–741 (1978)CrossRefGoogle Scholar
  11. 11.
    Woestengurg, J.C., Verbaten, M.N., Slangen, J.L.: The removal of the eye movement artifact from the EEG by regression analysis in the frequency domain. Biol. Physiol. 16, 127–147 (1983)Google Scholar
  12. 12.
    Lagerlund, T.D., Sharbrough, F.W., Busacker, N.E.: Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. Clin. Neurophysiol. 14(1), 73–82 (1997)CrossRefGoogle Scholar
  13. 13.
    Elbert, T., Lutzenberger, W., Rockstroh, B., Birbaumer, N.: Removal of ocular artifacts from the EEG-a biophysical approach to the EOG. Electroencephalogr. Clin. Neurophysiol. 60, 455–463 (1985)CrossRefGoogle Scholar
  14. 14.
    Shoker, L., Sanei, S., Latif, M.A.: Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Process Lett. 12(10), 721–724 (2005)CrossRefGoogle Scholar
  15. 15.
    Urretarazu, E., Iriarte, J., Alegre, M., Valencia, M., Vireri, C., Artieda, J.: Independent component analysis removing artifacts in ictal recordings. Epilepsia 45, 1071–1078 (2004)CrossRefGoogle Scholar
  16. 16.
    Pizzagalli, D.A.: Electroencephalography and high-density electrophysiological source localization. In: Cacioppo, J.T., Tassinary, G.G. (eds.) Bernston, Handbook of Psychophysiology, 3rd edn, pp. 56–84. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  17. 17.
    Krishnaveni, V., Jayaraman, S., Anitha, L., Ramadoss, K.: Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coe_cients. J. Neural Eng. 3, 338–346 (2006)CrossRefGoogle Scholar
  18. 18.
    Indiradevi, K.P., Elias, E., Sathidevi, P.S., Nayak, S.D., Radhakrishman, K.: A multilevel wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. J. Comput. Biol. Med. 38(7), 805–816 (2008)CrossRefGoogle Scholar
  19. 19.
    Zhou, W., Gotman, J.: Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA. In: Proceedings of 26th Annual International Conference IEEE Engineering in Medicine and Biology Society (EMBS04), San Francisco, CA, Sep. 15, pp. 392–395 (2004)Google Scholar
  20. 20.
    Nguyen, H.-A.T., Musson, J., Li, J., McKenzie, F., Zhang, G., Zu, R., Richey, C., Schell, T. EEG artifact removal using a Wavelet Neural Network. In Proceedings of Mod Sim World (2010)Google Scholar
  21. 21.
    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. Adv. Neural Inf. Process. Syst. 10, 894–900 (1998)Google Scholar
  22. 22.
    Vigario, R.N.: Extraction of ocular artifacts from EEG using independent component analysis. Electroencephalogr. Clin. Neurophysiol. 103, 395–404 (1997)CrossRefGoogle Scholar
  23. 23.
    He, P., Wilson, G., Russell, C.: Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med. Biol. Eng. Comput. 42, 407–412 (2004)CrossRefGoogle Scholar
  24. 24.
    Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 45, 434–444 (1997)CrossRefGoogle Scholar
  25. 25.
    Joyce, C.A., Gorodnitsky, I.F., Kutas, M.: Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 41(2), 313–325 (2004)CrossRefGoogle Scholar
  26. 26.
    Croft, R.J., Barry, R.J.: Removal of ocular artifact from the EEG: a review. Neurophysiol. Clin. 30, 5–19 (2000)CrossRefGoogle Scholar
  27. 27.
    Rowland, V.: Cortical steady potential (direct current potential) in reinforcement and learning. Progr. Physiol. Psychol. 2, 1–77 (1968)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Donghua UniversityShanghaiChina

Personalised recommendations

Cite article

Buy options