Skip to main content
SpringerLink
Log in

Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Electrooculogram (EOG) is one of the major artifacts in the design of electroencephalogram (EEG)-based brain computer interfaces (BCIs). That removing EOG artifacts automatically while retaining more neural data will benefit for further feature extraction and classification. In order to remove EOG artifacts automatically as well as reserve more useful information from raw EEG, this paper proposes a novel blind source separation method called CCA-EEMD (canonical correlation analysis, ensemble empirical mode decomposition). Technically, the major steps of CCA-EEMD are as follows: Firstly, the multiple-channel original EEG signals are separated into several uncorrelated components using CCA. Then, the EOG component can be identified automatically by its kurtosis value. Next, the identified EOG component is decomposed into several intrinsic mode functions (IMFs) by EEMD. The IMFs uncorrelated to the EOG component are recognized and retained, and a new component will be constructed by the retained IMFs. Finally, the clean EEG signals are reconstructed. Keep in mind that the novelty of this paper is that the identified EOG component is not removed directly but used to extract neural EEG data, which would keep more effective information. Our tests with the data of seven subjects demonstrate that the proposed method has distinct advantages over other two commonly used methods in terms of average root mean square error [37.71 ± 0.14 (CCA-EEMD), 44.72 ± 0.13 (CCA), 49.59 ± 0.16 (ICA)], signal-to-noise ratio [3.59 ± 0.24 (CCA-EEMD), −6.53 ± 0.18(CCA), −8.43 ± 0.26 (ICA)], and classification accuracy [0.88 ± 0.002 (CCA-EEMD), 0.79 ± 0.001 (CCA), 0.73 ± 0.002 (ICA)]. The proposed method can not only remove EOG artifacts automatically but also keep the integrity of EEG data to the maximum extent.

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

Similar content being viewed by others

References

  1. Lafleur K, Cassady K, Doud A, et al. Quadcopter control in three-dimensional space using a noninvasive motor imagery based brain-computer interface. J Neural Eng. 2013;10(4):711–26.

    Article  Google Scholar 

  2. Schlögl A, Keinrath C, Zimmermann D. A fully automated correction method of EOG artifacts in EEG recordings. Clinical Neurophysiology Official Journal of the International Federation of Clinical Neurophysiology. 2007;118(1):98–104.

    Article  PubMed  Google Scholar 

  3. Zhang X, Vialatte FB, Chen C, et al. Embedded implementation of second-order blind identification (SOBI) for real-time applications in neuroscience. Cogn Comput. 2015;7(1):1–8.

    Article  Google Scholar 

  4. Abdullah AK, Zhu ZC, Siyao L, et al. Blind source separation techniques based eye blinks rejection in EEG signals. Inf Technol J. 2014;13(3):401.

    Article  Google Scholar 

  5. Chatel-Goldman J, Congedo M, Phlypo R. Joint BSS as a natural analysis framework for EEG-hyperscanning. Acoustics, Speech and Signal Processing (ICASSP), 2013 I.E. International Conference on. IEEE. 2013;32(3):1212–1216.

  6. Quinn M, Mathan S, Pavel M. Removal of ocular artifacts from EEG using learned templates. Foundations of Augmented Cognition. 2013:371–80.

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

    Article  PubMed  Google Scholar 

  8. Sweeney KT, Mcloone SF, Ward TE. The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Trans Biomed Eng. 2013;60(1):97–105.

    Article  PubMed  Google Scholar 

  9. Borga M, Knutsson H. A canonical correlation approach to blind source separation. IEEE Pami Vol, 2001;1–12.

  10. Hassan M, Boudaoud S, Terrien J, et al. Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram. IEEE Trans Biomed Eng. 2011;58(9):2441–7.

    Article  PubMed  Google Scholar 

  11. Mowla MR, Ng SC, Zilany MSA, et al. Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising. Biomedical Signal Processing & Control. 2015;22:111–8.

    Article  Google Scholar 

  12. Lei Y, Zuo M-J. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs. Measurement Science & Technology. 2009;20(12):314–7.

    Article  CAS  Google Scholar 

  13. Clercq WD, Vergult A, Vanrumste B, et al. Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng. 2007;53(12 Pt 1):2583–7.

    Google Scholar 

  14. Brown SD. An introduction to multivariate statistical analysis. Appl Spectrosc. 2010;64:112–2.

  15. Yang B, Zhang Y, He L, et al. Removal of EOG artifacts from EEG signals in BCI based on ICA-RLS. Yi Qi Yi Biao Xue Bao/chinese Journal of Scientific Instrument. 2015;36(3):668–74.

    Google Scholar 

  16. Yu L, Wang S, Lai K-K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ. 2008;30(5):2623–35.

    Article  Google Scholar 

  17. Shen Z, Wang Q, Shen Y, et al. Accent extraction of emotional speech based on modified ensemble empirical mode decomposition. IEEE Instrumentation & Measurement Technology Conference. 2010:600–604.

  18. Wu Z-H, Huang N-E. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal. 2011;1(01):1–41.

    Article  Google Scholar 

  19. Blankertz B, Dornhege G, Krauledat M, et al. The non-invasive berlin brain–computer interface: fast acquisition of effective performance in untrained subjects. NeuroImage. 2007;37(2):539–50.

    Article  PubMed  Google Scholar 

  20. Yang B, Li H, Wang Q, et al. Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces. Computer Methods & Programs in Biomedicine. 2016;129(C):21–8.

    Article  Google Scholar 

  21. Zhang Y, Zhou G, Jin J, et al. Sparse Bayesian classification of EEG for brain-computer ssserface. IEEE Transactions on Neural Networks & Learning Systems. 2015;27(11):1–1.

    Google Scholar 

  22. Zhang Y. Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int J Neural Syst. 2017;27(2):1650032.

  23. Zhang Y, Zhou G, Jin J, et al. Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface. Int J Neural Syst. 2014;24(1):263–74.

    Article  Google Scholar 

  24. Solé-Casals J, Vialatte FB, Dauwels J. Alternative techniques of neural signal processing in neuroengineering. Cogn Comput. 2015;7(1):1–2.

    Article  Google Scholar 

  25. Jalili M. Multivariate synchronization analysis of brain electroencephalography signals: a review of two methods. Cogn Comput. 2015;7(1):3–10.

    Article  Google Scholar 

  26. Zhang Y, Zhou G, Zhao Q, et al. Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation. Neurocomputing. 2016;198:148–54.

    Article  Google Scholar 

  27. Zhou G, Cichocki A, Zhang Y, et al. Group component analysis for multiblock data: common and individual feature extraction. IEEE Trans Neural Netw Learn Syst. 2016;27(11):2426–2439.

  28. Zhang Y, Zhou G, Jin J, et al. Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis. Int J Neural Syst. 2014;24(4):1072–92.

    Article  Google Scholar 

  29. Martis RJ, Acharya UR, Tan JH, et al. Application of empirical mode decomposition (emd) for automated detection of epilepsy using EEG signals. Int J Neural Syst. 2012;22(6):809–27.

    Article  Google Scholar 

  30. Park C, Looney D, Ur Rehman N, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE Transactions on Neural Systems & Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society. 2013;21(1):10–22.

    Article  Google Scholar 

Download references

Acknowledgements

This project is supported by National Natural Science Foundation of China (60975079, 31100709).

Author information

Authors and Affiliations

  1. Department of Automation, School of Mechatronic Engineering and Automation, Key Laboratory of Power Station Automation Technology, Shanghai University, Dianji Building, Room 315, MailBox 8, No. 149, Yanchang Road, Zhabei District, Shanghai, 200072, China

    Banghua Yang, Tao Zhang, Yunyuan Zhang, Jianguo Wang & Kaiwen Duan

  2. Department of Computing, Curtin University, Perth, WA, 6102, Australia

    Wanquan Liu

Authors
  1. Banghua Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunyuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wanquan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianguo Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kaiwen Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Banghua Yang.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare that they have no conflict interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, B., Zhang, T., Zhang, Y. et al. Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition. Cogn Comput 9, 626–633 (2017). https://doi.org/10.1007/s12559-017-9478-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-017-9478-0

Keywords

Search

Navigation