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Practical Denoising of MEG Data Using Wavelet Transform

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Neural Information Processing (ICONIP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4233))

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Abstract

Magnetoencephalography (MEG) is an important noninvasive, non-hazardous technology for functional brain mapping, measuring the magnetic fields due to the intracellular neuronal current flow in the brain. However, the inherent level of noise in the data collection process is large enough to obscure the signal(s) of interest most often. In this paper, a practical denoising technique based on the wavelet transform and the multiresolution signal decomposition technique is presented. The proposed technique is substantiated by the application results using three different mother wavelets on the recorded MEG signal.

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References

  1. Paetau, R.: Magnetoencephalography in pediatric neuroimaging. Developmental Sciences 5, 361–370 (2002)

    Article  Google Scholar 

  2. Cohen, D.: Magnetoencephalography: evidence of magnetic field produced by alpha-rhythm currents. Science 164, 784–786 (1968)

    Article  Google Scholar 

  3. Zimmermann, J.E., Thiene, P., Harding, J.T.: Design and operation of stable rf-biased superconducting point-contact quantum devices and a note on the properties of perfectly clean metal contacts. Journal of Applied Physics 41, 1572–1580 (1970)

    Article  Google Scholar 

  4. Ikeda, S., Toyama, K.: Independent component analysis for noisy data-MEG data analysis. Neural Network 13, 1063–1074 (2000)

    Article  Google Scholar 

  5. de Munck, J.C., Bijma, F., Gaura, P., Sieluzycki, C.A., Branco, M.I., Heethaar, R.M.: A maximum-likelihood estimator for trial-to-trial variations in noisy MEG/EEG data sets. IEEE Transactions Biomedical Engineering 51, 2123–2128 (2004)

    Article  Google Scholar 

  6. Makeig, S., Jung, T.P., Bell, A.J., Ghahremani, D., Sejnowski, T.J.: Blind separation of auditory event-related brain responses into independent components. Proc. Natl. Acad. Sci. USA 94, 10979–10984 (1997)

    Article  Google Scholar 

  7. Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia (1992)

    MATH  Google Scholar 

  8. Mallat, S.: A wavelet tour of signal processing. Academic Press, New York (1998)

    MATH  Google Scholar 

  9. Mallat, S.: A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Analysis and Machine Intelligence 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  10. Strang, G., Nguyen, T.: Wavelets and filter banks. Wellesley-Cambridge Press, Wellesley-MA (1996)

    Google Scholar 

  11. Ukil, A., Zivanovic, R.: Adjusted Haar wavelet for application in the power systems disturbance analysis. Digital Signal Processing (under review)

    Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Ukil, A. (2006). Practical Denoising of MEG Data Using Wavelet Transform. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_65

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  • DOI: https://doi.org/10.1007/11893257_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46481-5

  • Online ISBN: 978-3-540-46482-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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