Advertisement

Methods for Removing of Line Noise Artifact from EEG Records with Minimization of Neural Information Loss

  • Jan StroblEmail author
  • Marek Piorecky
  • Vlastimil Koudelka
  • Tomas Nagy
  • Vladimir Krajca
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

Line noise is artifact affecting a gamma band of the EEG. Conventional filters are used for removing the line noise artifact, but these filters, also remove physiological activity and could induce spurious oscillations. We tested two alternative methods for removing line noise artifact without removing a physiological activity, specifically Adaptive noise cancellation (ANC) method with Linear regression and modified Independent Component Analysis (ICA). We proposed a new protocol for statistical evaluation of line noise removal effectiveness. The line noise artifact was physically simulated and the protocol for statistical evaluation was verified. The ANC method shows good results according to statistical evaluation. The lowest residuum of simulated line noise artifact corresponds to 0.0005 \(\upmu \)V\(^2\)/Hz. Nevertheless, the ANC is very sensitive to the quality of the reference signal, which heavily depends on the reference electrode selection.

Keywords

Line noise artifact EEG ICA ANC Linear regression Statistical evaluation 

Notes

Acknowledgements

This work was supported by the Grant Agency of the Czech Technical University in Prague, registr. numb. SGS19/136/OHK4/2T/17, by the Grant Agency of the Czech Technical University in Prague, registr. numb. SGS18/159/OHK4/2T/17, by the Grant Agency of Czech Republic, register number 17-20480S, by the Grant Agency of Czech Republic, register number 19-15728S and project LO1611 with a financial support from the MEYS under the NPU I program.

Statements by Authors

The authors declare that there is no conflict of interest regarding the publication of this article. The study protocol and patient informed consent have been approved by National Institute of Mental Health ethical committee. The procedures followed were in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects.

References

  1. 1.
    Aspiras, T.H., Asari, V.K.: Analysis of spatio-temporal relationship of multiple energy spectra of EEG data for emotion recognition. In: Computer Networks and Intelligent Computing, pp. 572–581. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22786-8_73Google Scholar
  2. 2.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004).  https://doi.org/10.1016/j.jneumeth.2003.10.009CrossRefGoogle Scholar
  3. 3.
    Chatterjee, S., Hadi, A.S.: Influential observations, high leverage points, and outliers in linear regression. Stat. Sci. 1(3), 379–393 (1986).  https://doi.org/10.1214/ss/1177013622CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Hermann, C.S., Fründ, I., Lenz, D.: Human gamma-band activity: a review on cognitive and behavioral correlates and network models. Neurosci. Biobehav. Rev. 34(7), 981–992 (2010).  https://doi.org/10.1016/j.neubiorev.2009.09.001CrossRefGoogle Scholar
  5. 5.
    Laglois, D., Chartier, S., Gosselin, D.: An introduction to independent component analysis: InfoMax and FastICA algorithms. Tutorials Quant. Methods Psychol. 6(1), 31–38 (2010).  https://doi.org/10.20982/tqmp.06.1.p031CrossRefGoogle Scholar
  6. 6.
    Lee, S.-H., Kim, S., Shim, M.-S., Kim, D.-W., Im, C.-H.: Dysfunctional patterns of gamma-band activity in response to human faces compared to non-facial stimuli in patients with schizophrenia. Psychiatry Invest. 13(3), 349–359 (2016).  https://doi.org/10.4306/pi.2016.13.3.349CrossRefGoogle Scholar
  7. 7.
    Nierhaus, T., Gundlach, C., Goltz, D., Thiel, S.D., Pleger, B., Villringer, A.: Internal ventilation system of MR scanners induces specific EEG artifact during simultaneous EEG-fMRI. NeuroImage 74, 70–76 (2013).  https://doi.org/10.1016/j.neuroimage.2013.02.016CrossRefGoogle Scholar
  8. 8.
    Nottage, J.F.: Uncovering gamma in visual tasks. Brain Topogr. 23(1), 58–71 (2010).  https://doi.org/10.1007/s10548-009-0129-yCrossRefGoogle Scholar
  9. 9.
    Shen, K., Yu, K., Bandla, A., Sun, Y., Thakor, N., Li, X.: Multiple time-lag canonical correlation analysis for removing muscular artifacts in EEG. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6792–6795 (2013).  https://doi.org/10.1109/EMBC.2013.6611116
  10. 10.
    Wang, Z., Roe, A.W.: Trial-to-trial noise cancellation of cortical field potentials in awake macaques by autoregression model with exogenous input (ARX). J. Neurosci. Methods 194(2), 266–273 (2011).  https://doi.org/10.1016/j.jneumeth.2010.10.029CrossRefGoogle Scholar
  11. 11.
    Zhou, W., Zhou, J., Zhao, J., Zhao, H., Ju, L.: Removing eye movement and power line artifacts from the EEG based on ICA. In: IEEE Engineering in Medicine and Biology 27th Annual Conference, vol. 6, pp. 6017–6020 (2005).  https://doi.org/10.1109/IEMBS.2005.1615863

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jan Strobl
    • 1
    • 2
    Email author
  • Marek Piorecky
    • 1
    • 2
  • Vlastimil Koudelka
    • 2
  • Tomas Nagy
    • 1
  • Vladimir Krajca
    • 1
    • 2
  1. 1.Department of Biomedical Technology, Faculty of Biomedical EngineeringCzech Technical University in PragueKladnoCzech Republic
  2. 2.National Institute of Mental HealthKlecanyCzech Republic

Personalised recommendations