Skip to main content

Advertisement

Log in

Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation

  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity.

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.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Antervo, A., R. Hari, T. Katila, T. Ryhänen, and M. Seppänen. Magnetic fields produced by eye blinking. Electroencephalogr. Clin. Neurophysiol. 61(4):247–253, 1985.

    Article  PubMed  CAS  Google Scholar 

  2. Barbati, G., C. Porcaro, F. Zappasodi, P. M. Rossini, and F. Tecchio. Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals. Clin. Neurophysiol. 115(5):1220–1232, 2004.

    Article  PubMed  Google Scholar 

  3. Belouchrani, A., K. Abed-Meraim, J. F. Cardoso, and E. Moulines. A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 45(2):434–444, 1997.

    Article  Google Scholar 

  4. Cardoso, J. F., and A. Souloumiac. Blind beamforming for non Gaussian signals. IEE Proc. F 140(6):362–370, 1993.

    Google Scholar 

  5. Cichocki, A., S. Amari, K. Siwek, T. Tanaka, and A. Huy Phan. ICALAB for Signal Processing. November 2010. [online] http://www.bsp.brain.riken.jp/ICALAB/ICALABSignalProc.

  6. Croft, R. J., and R. J. Barry. Removal of ocular artifact from the EEG: a review. Neurophysiol. Clin. 30(1):5–19, 2000.

    Article  PubMed  CAS  Google Scholar 

  7. Dammers, J., M. Schiek, F. Boers, C. Silex, M. Zvyagintsev, U. Pietrzyk, and K. Mathiak. Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings. IEEE Trans. Biomed. Eng. 55(10):2353–2362, 2008.

    Article  PubMed  Google Scholar 

  8. Delorme, A., and S. Makeig. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1):9–21, 2004.

    Article  PubMed  Google Scholar 

  9. Delorme, A., T. Sejnowski, and S. Makeig. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 34(4):1443–1449, 2007.

    Article  PubMed  Google Scholar 

  10. Escudero, J., R. Hornero, and D. Abásolo. Consistency of the blind source separation computed with five common algorithms for magnetoencephalogram background activity. Med. Eng. Phys. 32(10):1137–1144, 2010.

    Article  PubMed  Google Scholar 

  11. Escudero, J., R. Hornero, D. Abásolo, and A. Fernández. Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer’s disease. Med. Eng. Phys. 31(7):872–879, 2009.

    Article  PubMed  Google Scholar 

  12. Escudero, J., R. Hornero, D. Abásolo, A. Fernández, and M. López-Coronado. Artifact removal in magnetoencephalogram background activity with independent component analysis. IEEE Trans. Biomed. Eng. 54(11):1965–1973, 2007.

    Article  PubMed  Google Scholar 

  13. Escudero, J., R. Hornero, J. Poza, D. Abásolo, and A. Fernández. Assessment of classification improvement in patients with Alzheimer’s disease based on magnetoencephalogram blind source separation. Artif. Intell. Med. 43(1):75–85, 2008.

    Article  PubMed  Google Scholar 

  14. Fatourechi, M., A. Bashashati, R. K. Ward, and G. E. Birch. EMG and EOG artifacts in brain computer interface systems: a survey. Clin. Neurophysiol. 118(3):480–494, 2007.

    Article  PubMed  Google Scholar 

  15. Fitzgibbon, S. P., D. M. W. Powers, K. J. Pope, and C. R. Clark. Removal of EEG noise and artifact using blind source separation. J. Clin. Neurophysiol. 24(3):232–243, 2007.

    Article  PubMed  CAS  Google Scholar 

  16. Gävert, H., J. Hurri, J. Särelä, and A. Hyvärinen. FastICA Toolbox. November 2010. [online] http://www.cis.hut.fi/projects/ica/fastica.

  17. Greco, A., N. Mammone, F. C. Morabito, and M. Versaci. Kurtosis, Renyi’s entropy and independent component scalp maps for the automatic artifact rejection from eeg data. Int. J. Signal Process. 2(4):240–244, 2006.

    Google Scholar 

  18. Hämäläinen, M., R. Hari, R. J. Ilmoniemi, J. Knuutila, and O. V. Lounasmaa. Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65(2):413–497, 1993.

    Article  Google Scholar 

  19. Huang, D. S., and J. X. Mi. A new constrained independent component analysis method. IEEE Trans. Neural Netw. 18(5):1532–1535, 2007.

    Article  PubMed  Google Scholar 

  20. Hyvärinen, A., J. Karhunen, and E. Oja. Independent Component Analysis. Hoboken, NJ: John Wiley & Sons, 2001.

    Book  Google Scholar 

  21. James, C. J., and O. J. Gibson OJ. Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis. IEEE Trans. Biomed. Eng. 50(9):1108–1116, 2003.

    Article  PubMed  Google Scholar 

  22. James, C. J., and C. W. Hesse. Independent component analysis for biomedical signals. Physiol. Measure. 26(1):R15–R39, 2005.

    Article  Google Scholar 

  23. Jousmäki, V., and R. Hari. Cardiac artifacts in magnetoencephalogram. J. Clin. Neurophysiol. 13(2):172–176, 1996.

    Article  PubMed  Google Scholar 

  24. Joyce, C. A., I. F. Gorodnitsky, and M. Kutas. Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 41(2):313–325, 2004.

    Article  PubMed  Google Scholar 

  25. Jung, T. P., S. Makeig, C. Humphries, T. W. Lee, M. J. Mckeown, V. Iragui, and T. J. Sejnowski. Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37(2):163–178, 2000.

    Article  PubMed  CAS  Google Scholar 

  26. Lee, T. W., M. Girolami, and T. J. Sejnowski. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources. Neural Comput. 11(2):417–441, 1999.

    Article  PubMed  CAS  Google Scholar 

  27. LeVan, P., E. Urrestarazu, and J. Gotman. A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification. Clin. Neurophysiol. 117(4):912–927, 2006.

    Article  PubMed  CAS  Google Scholar 

  28. Li, Y., Z. Ma, W. Lu, and Y. Li. Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiol. Measure. 27(4):425–436, 2006.

    Article  Google Scholar 

  29. Lu, W., and J. C. Rajapakse JC. Approach and applications of constrained ICA. IEEE Trans. Neural Netw. 16(1):203–212, 2005.

    Article  PubMed  Google Scholar 

  30. Mammone, N., and F. C. Morabito. Enhanced automatic artifact detection based on independent component analysis and Renyi’s entropy. Neural Netw. 21(7):1029–1040, 2008.

    Article  PubMed  Google Scholar 

  31. Mantini, D., R. Franciotti, G. L. Romani, and V. Pizzella. Improving MEG source localizations: An automated method for complete artifact removal based on independent component analysis. NeuroImage 40(1):160–173, 2008.

    Article  PubMed  CAS  Google Scholar 

  32. Okada, Y., J. Jung, and T. Kobayashi. An automatic identification and removal method for eye-blink artifacts in event-related magnetoencephalographic measurements. Physiol. Measure. 28(12):1523–1532, 2007.

    Article  CAS  Google Scholar 

  33. Romero, S., M. A. Mañanas, and M. J. Barbanoj. A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a simulation case. Comput. Biol. Med. 38(3):348–360, 2008.

    Article  PubMed  Google Scholar 

  34. Romero, S., M. A. Mañanas, and M. J. Barbanoj. Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation. Ann. Biomed. Eng. 37(1):176–191, 2009.

    Article  PubMed  CAS  Google Scholar 

  35. Shao, S., K. Shen, C. Ong, E. Wilder-Smith, and X. Li. Automatic EEG artifact removal: a weighted support-vector-machine approach with error correlation. IEEE Trans. Biomed. Eng. 56(2):336–344, 2008.

    Article  PubMed  Google Scholar 

  36. Ting, K. H., P. C. W. Fung, C. Q. Chang, and F. H. Y. Chan. Automatic correction of artifact from single-trial event-related potentials by blind source separation using second order statistics only. Med. Eng. Phys. 28(8):780–794, 2006.

    Article  PubMed  CAS  Google Scholar 

  37. Tong, L., R. W. Liu, V. C. Soon, and Y. F. Huang. Indeterminacy and identifiability of blind identification. IEEE Trans. Circuits Syst. 38(5):499–509, 1991.

    Article  Google Scholar 

  38. Vialatte, F. B., J. Solé-Casals, M. Maurice, C. Latchoumane, N. Hudson, S. Wimalaratna, J. Jeong, and A. Cichocki. Improving the quality of EEG data in patients with Alzheimer’s disease using ICA. Lect. Notes Comput. Sci. 5507:979–986, 2009.

    Article  Google Scholar 

  39. Vigário, R., and E. Oja. BSS and ICA in neuroinformatics: from current practices to open challenges. IEEE Rev. Biomed. Eng. 1:50–61, 2008.

    Article  Google Scholar 

  40. Zavala Fernández, H., T. H. Sander, M. Burghoff, R. Orglmeister, and L. Trahms. Comparison of ICA algorithms for the isolation of biological artifacts in magnetoencephalography. Lect. Notes Comput. Sci. 3889:511–518, 2006.

    Article  Google Scholar 

Download references

Acknowledgments

This study was partially supported by the “Ministerio de Ciencia e Innovación” and FEDER grant TEC2008-02241. We are thankful to the three Reviewers of this manuscript for their useful comments.

Conflict of interest

There are no conflicts of interest that could inappropriately influence this manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Javier Escudero.

Additional information

Associate Editor Nathalie Virag oversaw the review of this article.

Electronic supplementary material

Below is the link to the electronic supplementary material.

PDF (90 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Escudero, J., Hornero, R., Abásolo, D. et al. Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation. Ann Biomed Eng 39, 2274–2286 (2011). https://doi.org/10.1007/s10439-011-0312-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-011-0312-7

Keywords

Navigation