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

  • Conference paper
Brain Informatics and Health (BIH 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8609))

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Abstract

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.

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References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Vigário, R.N.: Extraction of ocular artifacts from EEG using independent component analysis. Electroencephalography and Clinical Neurophysiology 103, 395–404 (1997)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation 9(7), 1483–1492 (1997)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  11. Croft, R.J., Barry, R.J.: Removal of ocular artifact from the EEG: a review. Neurophysiologie Clinique/Clinical Neurophysiology 30, 5–19 (2000)

    Article  Google Scholar 

  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. 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. Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, New Jersey (1985)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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 

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Huang, R. et al. (2014). Artifacts Reduction Method in EEG Signals with Wavelet Transform and Adaptive Filter. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-09891-3_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09890-6

  • Online ISBN: 978-3-319-09891-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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