Skip to main content
Log in

An approach to removing spatially uncorrelated artifacts from EEG recordings

  • Methods
  • Published:
Human Physiology Aims and scope Submit manuscript

Abstract

This work deals with elimination artifacts from electroencephalograms (EEGs). Though many methods for solving the problem have been proposed, they either require direct intervention of the researcher, are based on additional measurements (electrooculogram, electrocardiogram, etc.), or cannot remove artifact activity to a sufficient extent. We proposed an automated method that uses of the fact that the electromyogram (EMG) is not correlated and the artifacts of electrode movement at adjacent derivations and is based on the representation of electrical activity in one derivation through activity in other derivations. The analysis of the proposed method using model signals and examples of real EEG recordings has demonstrated its practical effectiveness.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ille, N., Berg, P., and Scherg, M., Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies, J. Clin. Neurophysiol., 2002, vol. 19, no. 2, p. 113.

    Article  PubMed  Google Scholar 

  2. Schlögl, A., Keinrath., C., Zimmermann, D., et al., A Fully Automated Correction Method of EOG Artifacts in EEG Recordings, Clin. Neurophysiol., 2007, vol. 118, no. 1, p. 98.

    Article  PubMed  Google Scholar 

  3. Erfanian, A., and Mahmoudi., B., Real-Time Ocular Artifact Suppression Using Recurrent N Neural Network for Electro-Encephalogram Based Brain Computer Interface, Med. Biol. Eng. Comput., 2005, vol. 43, no. 2, p. 296.

    Article  PubMed  CAS  Google Scholar 

  4. Jiang, J.A., Chao, C.F., Chiu M.J., et al., An Automatic Analysis Method for Detecting and Eliminating ECG Artifacts in EEG, Comput. Biol. Med., 2007, vol. 37, no. 11, p. 1660.

    Article  PubMed  Google Scholar 

  5. James, C.J., and Hesse, C.W., Independent Component Analysis for Biomedical Signals, Physiol. Meas., 2005, vol. 26, no. 1, p. 15.

    Article  Google Scholar 

  6. Vigário, R., Särelä, J., Jousmäki, V., et al., Independent Component Approach to the Analysis of EEG and MEG Recordings, IEEE Trans. Biomed. Eng., 2000, vol. 47, no. 5, p. 589.

    Article  PubMed  Google Scholar 

  7. Castellanos, N.P., and Makarov, V.A., Recovering EEG Brain Signals: Artifact Suppression with Wavelet Enhanced Independent Component Analysis, J. Neurosci. Methods, 2006, vol. 158, no. 2, p. 300.

    Article  PubMed  Google Scholar 

  8. James, C.J., and Gibson, O.J., Temporary Constrained ICA: An Application to Artifact Rejection in Electromagnetic Brain Signal Analysis, IEEE Trans. Biomed. Eng., 2003, vol. 50, no. 9, p. 1108.

    Article  PubMed  Google Scholar 

  9. Ghanderson, H., and Erfman., A., A Fully Automatic Method for Ocular Artifact Suppression from EEG Data Using Wavelet Transform and Independent Component Analysis, Conf. Proc. IEEE Eng. Med. Biol. Soc., 2006, no. 1, p. 5265.

  10. Le Van, P., Urrestarazu, E., and Gotman, J., A System for Automatic Artifact Removal in Ictal Scalp EEG Based on Independent Component Analysis and Bayesian Classification, Clin. Neurophysiol., 2006, vol. 117, no. 4, p. 912.

    Article  Google Scholar 

  11. Buchthal, F., and Madsen, A., Synchronous Activity in Normal and Atrophic Muscle, EEG Clin. Neurophysiol., 1950, vol. 2, no. 4, p. 425.

    Article  CAS  Google Scholar 

  12. Ivanov, L.B., and Shaligin, V.S., Raspoznavanie artefaktov i nekotorie slozhnosti prakticheskogo analiza komp’iuternoi EEG (Artifact Recognition and Some Difficulties in Practical Analysis of Computer EEG), Moscow: MBN, 2007.

    Google Scholar 

  13. Seber, J.A.F., Linear Regression Analysis, John Wiley and Sons, 1977.

  14. Bell, A.J. and Sejnowski, T.J., An Informational Maximization Approach to Blind Separation and Blind Deconvolution, Neur. Comput., 1995, no. 7, p. 1129.

  15. Makeig, S., Bell, A.J., Jung, T.-P., and Sejnowsky, T.J., Independent Component Analysis of Electroencephalographic Data, in Advances in Neural Information Processing Systems, Tourezky, D., Mozer, M., and Hasselmo, M., Eds, Cambridge: MIT Press, 1996, vol. 8, p. 145.

    Google Scholar 

  16. Makeig, S., EEGlab: ICA Toolbox for Physiological Research, www Site, Swartz Center for Computational Neuroscience, Institute of Neural Computation, University of San Diego California 〈www.sccn.ucsd.edu/ eeglab/〉, 2000 [World Wide Web Publication].

Download references

Author information

Authors and Affiliations

Authors

Additional information

Original Russian Text © E.L. Masherov, P.E. Volynsky, G.A. Shekut’ev, 2009, published in Fiziologiya Cheloveka, 2009, Vol. 35, No. 4, pp. 124–134.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Masherov, E.L., Volynsky, P.E. & Shekut’ev, G.A. An approach to removing spatially uncorrelated artifacts from EEG recordings. Hum Physiol 35, 502–512 (2009). https://doi.org/10.1134/S0362119709040161

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0362119709040161

Keywords

Navigation