Artifacts Reduction Method in EEG Signals with Wavelet Transform and Adaptive Filter

  • Rui Huang
  • Fei Heng
  • Bin Hu
  • Hong Peng
  • Qinglin Zhao
  • Qiuxia Shi
  • Jun Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)


This paper presents a method to remove ocular artifacts from electroencephalograms (EEGs) which can be used in biomedical analysis in portable environment. An important problem in EEG analysis is how to remove the ocular artifacts which wreak havoc among analyzing EEG signals. In this paper, we propose a combination of Wavelet Transform with effective threshold and adaptive filter which can extract the reference signal according to ocular artifacts distributing in low frequency domain mostly, and adaptive filter based on Least Mean Square (LMS) algorithm is used to remove ocular artifacts from recorded EEG signals. The results show that this method can remove ocular artifacts and superior to a comparison method on retaining uncontaminated EEG signal. This method is applicable to the portable environment, especially when only one channel EEG are recorded.


electroencephalogram (EEG) ocular artifacts adaptive filter signal processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jung, T.P., Makeig, S., Humphries, C., Lee, T.W., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 37, 163–178 (2000)CrossRefGoogle Scholar
  2. 2.
    Gratton, G., Coles, M.G.H., Donchin, E.: A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology 55(4), 468–484 (1983)CrossRefGoogle Scholar
  3. 3.
    Woestengurg, J.C., Verbaten, M.N., Slangen, J.L.: The removal of the eye movement artifact from the EEG by regression analysis in the frequency domain. Biological Physiology 16, 127–147 (1982)Google Scholar
  4. 4.
    Vigário, R.N.: Extraction of ocular artifacts from EEG using independent component analysis. Electroencephalography and Clinical Neurophysiology 103, 395–404 (1997)CrossRefGoogle Scholar
  5. 5.
    Hu, S., Stead, M., Worrell, G.A.: Automatic Identification and Removal of Scalp Reference Signal for Intracranial EEGs Based on Independent Component Analysis. IEEE Trans. Biomed. Eng. 54(9), 1560–1572 (2007)CrossRefGoogle Scholar
  6. 6.
    Vigario, R., Sarela, J., Jousmaki, V., Hamalainen, M., Oja, E.: Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans. Biomed. Eng. 47(5), 589–593 (2000)CrossRefGoogle Scholar
  7. 7.
    Lu, W., Rajapakse, J.C.: ICA with reference. In: Proc. 3rd Int. Conf. Independent Component Analysis and Blind Signal Separation: ICA 2001, pp. 120–125 (2001)Google Scholar
  8. 8.
    Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9(7), 1483–1492 (1997)CrossRefGoogle Scholar
  9. 9.
    Shen, K.-Q., Ong, C.J., Wilder-Smith, E., Li, X.-P.: Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction. IEEE Trans. Biomed. Eng. 56(2), 336–344 (2009)CrossRefGoogle Scholar
  10. 10.
    Lins, O.G., Picton, T.W., Berg, P., Scherg, M.: Ocular artifacts in EEG and event-related potentials, I: Scalp topography. Brain Topography 6(1), 51–63 (1993)CrossRefGoogle Scholar
  11. 11.
    Croft, R.J., Barry, R.J.: Removal of ocular artifact from the EEG: a review. Neurophysiologie Clinique/Clinical Neurophysiology 30, 5–19 (2000)CrossRefGoogle Scholar
  12. 12.
    Peng, H., Hu, B., Qi, Y., Zhao, Q., Ratcliffe, M.: An Improved EEG De-noising Approach in Electroencephalogram (EEG) for Home Care. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops, pp. 469–474 (May 2011)Google Scholar
  13. 13.
    Krishnaveni, V., Jayaraman, S., Aravind, S., Hariharasudhan, V., Ramadoss, K.: Automatic Identification and Removal of Ocular Artifacts from EEG Using Wavelet Transform. Measurement Science Review 6(4), 45–57 (2006)Google Scholar
  14. 14.
    Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, New Jersey (1985)zbMATHGoogle Scholar
  15. 15.
    He, P., Wilson, G., Russell, C.: Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med. Biol. Eng. Comput. 42, 407–412 (2004)CrossRefGoogle Scholar
  16. 16.
    Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: Components of a New Research Resource for Complex Physiologic Signals. Circulation (June 2000)Google Scholar
  17. 17.
    Krishnaveni, V., Jayaraman, S., Malmurugan, N., Kandasamy, A., Ramadoss, D.: Non adaptive thresholding methods for correcting ocular artifacts in EEG. Academic Open Internet Journal 13 (2004)Google Scholar
  18. 18.
    Hu, B., Majoe, D., Ratcliffe, M., Qi, Y., Zhao, Q., Peng, H., Fan, D., Zheng, F., Jackson, M., Moore, P.: EEG-Based Cognitive Interfaces for Ubiquitous Applications: Developments and Challenges, vol. 26, pp. 46–53 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rui Huang
    • 1
  • Fei Heng
    • 1
  • Bin Hu
    • 1
    • 2
  • Hong Peng
    • 1
  • Qinglin Zhao
    • 1
  • Qiuxia Shi
    • 1
  • Jun Han
    • 3
  1. 1.The School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.The School of Electronic Information and Control EngineeringBeijing University of TechnologyBeijingChina
  3. 3.Chinese Academy of SciencesChina

Personalised recommendations