Skip to main content
SpringerLink
Log in

Removal of eye blinking artifact from the electro-encephalogram, incorporating a new constrained blind source separation algorithm

  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

A robust constrained blind source separation (CBSS) algorithm has been developed as an effective means to remove ocular artifacts (OAs) from electroencephalograms (EEGs). Currently, clinicians reject a data segment if the patient blinked or spoke during the observation interval. The rejected data segment could contain important information masked by the artifact. In the CBSS technique, a reference signal was exploited as a constraint. The constrained problem was then converted to an unconstrained problem by means of non-linear penalty functions weighted by the penalty terms. This led to the modification of the overall cost function, which was then minimised with the natural gradient algorithm. The effectiveness of the algorithm was also examined for the removal of other interfering signals such as electrocardiograms. The CBSS algorithm was tested with ten sets of data containing OAs. The proposed algorithm yielded, on average, a 19% performance improvement over Parra's BSS algorithm for removing OAs.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bell, A. J. andSejnowski, T. J. (1995): ‘An information-maximization approach to blind separation and blind deconvolution’,Neural Comput.,7, pp. 1129–1159

    Google Scholar 

  • Bewrg, P., andScherg, M. (1994): ‘Dipole modeling of eye activity and its application to the removal of eye artifacts from EEG and MEG’,Clin. Physiol. Physiol. Meas.,12, pp. 49–54

    Google Scholar 

  • Celka, P., Boashash, B., andColditz, P., (2001): ‘Preprocessing and time-frequency analysis of newborn EEG seizures’,IEEE Trans. Neural Netw.,20, pp. 30–39

    Google Scholar 

  • Cichocki, A., andAmari, S. (2002): ‘Adaptive blind signal and image processing: learning algorithms and applications’ (John Wiley & Sons Ltd, 2002)

  • Elbert, T., Lutzenberger, W., Rockstroh, B., andBirbaumer, N. (1985): ‘Removal of occular artifacts from the EEG—a biophysical approach to the EOG’,Electroencephalogr. Clin. Neurophysiol.,60, pp. 455–463

    Google Scholar 

  • Gratton, G. (1998): ‘Dealing with artifacts: The EOG contamination of the event-related brain potential’,Behav. Res. Methods, Instrum. Comput.,30, pp. 40–41

    Google Scholar 

  • Haykin, S. (2002): ‘Adaptive filter theory, 4th edn’ (Prentice-Hall, 2002)

  • He, P., Wilson, G., andRussell, C. (2004): ‘Removal of ocular artifacts from electro-encephalogram by adaptive filtering’,Med. Biol. Eng. Comput.,42, pp. 407–412

    Article  Google Scholar 

  • Hyvarinen, A., Oja, E., andKarhunen, J. (2001): ‘Independent component analysis (adaptive & learning systems for signal processing, communications & control S.)’ (Thomson Learning, Inc, 2001)

  • Joyce, C. A., Gorodnitsky, I., andKautas, M. (2004): ‘Automatic removal of eye movement and blink artifacts from EEG data using blind component separation’,Psychophysiology,41, pp. 313–325

    Article  Google Scholar 

  • Jung, T., Makeig, S., McKeown, M., Bell, A., Lee, T., andSejnowski, T. J. (2001): ‘Imaging brain dynamics using independent component analysis’,IEEE Procses.,89, pp. 1107–1122

    Google Scholar 

  • Lu, W., andRajapakse, J. (2001): ‘ICA with reference’. Proc. 3rd Int. Conf. Independent Component Analysis & Blind Signal Separation, ICA2001, pp. 120–125

  • Overton, D. A., andShagass, C. (1969): ‘Distribution of eye movement and eyeblink potentials over the scalp’,Electroenceph. Clin. Neurophysiol.,27, p. 546

    Google Scholar 

  • Parra, L., andSpence, C. (2000): ‘Convolutive blind separation of non-stationary sources’,IEEE Trans. Speech Audio Process.,8, pp. 320–327

    Article  Google Scholar 

  • Schlogl, A., andPfurtsheller, G. (1999): ‘EOG and ECG minimization based on regression analysis’. Technical Report, Institute for Biomedical Engineering

  • Smaragdis, P. (1997): ‘Information theoretic approaches to source separation’. Masters thesis, MIT Media Lab

  • Wang, W., Sanei, S., andChambers, J. A. (2005): ‘Penalty function based joint diagonalization approach for convolutive blind separation of nonstationary sources’,IEEE Trans. Signal Process, (to be published).

  • Wagner, G. S., andMarriott, H. J. L. (2001): ‘Marriott's practical electrocardiography’, 10th edn (Lippincott Williams & Wilkins, 2001)

Download references

Author information

Authors and Affiliations

  1. Centre for Digital Signal Processing, Cardiff School of Engineering, Cardiff University, Cardiff, UK

    L. Shoker, S. Sanei, W. Wang & J. A. Chambers

Authors
  1. L. Shoker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Sanei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. W. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. A. Chambers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to L. Shoker.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shoker, L., Sanei, S., Wang, W. et al. Removal of eye blinking artifact from the electro-encephalogram, incorporating a new constrained blind source separation algorithm. Med. Biol. Eng. Comput. 43, 290–295 (2005). https://doi.org/10.1007/BF02345968

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02345968

Keywords

Search

Navigation