Skip to main content

Advertisement

SpringerLink
Log in

Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

As the electroencephalography (EEG) biomedical signals are affected under the presence of the muscular motion artifacts. Presence of these artifacts leads to error in visual analysis of EEG signal, thus results in wrong diagnosis of human diseases. The variants of blind source separation (BSS) methods are available. This paper aims to design the efficient BSS based method for effectively eradicating the EEG motion artifacts. This is accomplished by evaluating the six different methods, which are combination of independent component analysis (ICA) and canonical correlation analysis (CCA) along with the discrete wavelet transform and stationary wavelet transform methods. Each of above combination methods are applied on the ensemble empirical mode decomposed, Intrinsic Mode Functions for EEG motion artifact suppression. This research paper tests the performance over pure EEG signal and also on the simulated EEG sinusoids to mimic the effect of motion artifacts. The performance of six BSS artifact removal algorithms are evaluated using efficiency matrices such as del signal to noise ratio, lambda (λ), spectral distortion (Pdis) and root mean square error. The execution time is also calculated to evaluate the computation efficiency of the algorithms. The results suggest that CCA algorithm outperforms over ICA in the case of the high noisy condition of EEG signal.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Paans, A. M. J. (2006). Positron emission tomography (pp. 363–382). https://cds.cern.ch/record/1005065/files/p363.pdf. Accessed 14 Dec 2006.

  2. Sweeney, K. T., & Ayaz, H. (2012). A Methodology for validating artifact removal techniques for physiological signals. IEEE Transactions, Information Technology In Biomedicine, 16(5), 918–926.

    Article  Google Scholar 

  3. Ng, S. C., & Raveendran, P. (2009). Enhanced µ rhythm extraction using blind source separation and wavelet transform. IEEE Transactions on Biomedical Engineering, 56(8), 2024–2034.

    Article  Google Scholar 

  4. Congedo, M. P., & Jutten, C. (2008). On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clinical Neurophysiology, 119(12), 2677–2686.

    Article  Google Scholar 

  5. Nam, H. Y., & Han, S. (2002). Independent component analysis of ictal EEG in medial temporal lobe epilepsy. Epilepsia, 43, 160–164.

    Article  Google Scholar 

  6. Urrestarazu, E. I., & Alegre, M. (2008). Independent component analysis removing artifacts in ictal recordings. Epilepsia, 45, 1071–1078.

    Article  Google Scholar 

  7. Clercq, W. D., & Vanrumste, B. (2006). Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Transaction Biomedical Engineering, 53, 2583–2587.

    Article  Google Scholar 

  8. Gao, J. Z., & Wang, P. (2010). Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis. Clinical EEG Neuroscience, 41, 53–59.

    Article  Google Scholar 

  9. Sweeney, K. T., & Ward, T. E. (2013). The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Transactions: Biomedical Engineering.

    Book  Google Scholar 

  10. Mijovic, B. V., & Gligorijevic, I. (2010). Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Transactions on Biomedical Engineering, 57(9), 2188–2196.

    Article  Google Scholar 

  11. Hyvärinen, A., & Erkki, O. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7), 1483–1492.

    Article  Google Scholar 

  12. Roy, V., & Shukla, S. (2015). A NLMS based approach for artifacts removal in multichannel EEG signals with ICA and double density wavelet transform. In International conference on CSNT (pp. 461-466). Gwaliar: IEEE Xplore.

  13. Friman, O. B., & Lundberg, P. (2002). Exploratory fMRI analysis by autocorrelation maximization. NeuroImage, 16(2), 454–464.

    Article  Google Scholar 

  14. Soomro, M. H., & Yusoff, M. (2014). Comparison of blind source separation methods for removal of eye blink artifacts from EEG. In Intelligent and advanced systems (ICIAS) (pp. 1–6).

  15. PhysioNet-motion artifact contaminated EEG and EEG data (motion artifact). (2012). http://physionet.org/cgi-bin/atm/ATM. Accessed 1 Oct 2015.

  16. Comon, P., & Jutten, C. (2010). Handbook of blind source separation. New York: Academic Press.

    Google Scholar 

  17. Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321–327.

    Article  MATH  Google Scholar 

  18. Raveendran, P., & Ng, S. C. (2009). Enhanced µ rhythm extraction using blind source separation and wavelet transform. IEEE Transactions on Biomedical Engineering, 56(8), 2024–2034.

    Article  Google Scholar 

  19. Khatun, S. M., & Morshed, B. I. (2015). Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG. In Electro/information technology (EIT) (pp. 335–340).

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Hitkarini College of Engineering and Technology, Jabalpur, 482002, India

    Vandana Roy

  2. Department of Computer Sciences Engineering, Jabalpur Engineering College, Jabalpur, 482005, India

    Shailja Shukla

Authors
  1. Vandana Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shailja Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vandana Roy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Roy, V., Shukla, S. Designing Efficient Blind Source Separation Methods for EEG Motion Artifact Removal Based on Statistical Evaluation. Wireless Pers Commun 108, 1311–1327 (2019). https://doi.org/10.1007/s11277-019-06470-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06470-3

Keywords

Search

Navigation