• Title of article

    Noise Attenuation Estimation for Maximum Length Sequences in Deconvolution Process of Auditory Evoked Potentials

  • Author/Authors

    Peng, Xian School of Biomedical Engineering - Southern Medical University - Guangzhou - Guangdong, China , Chen, Yun’er School of Biomedical Engineering - Southern Medical University - Guangzhou - Guangdong, China , Wang, Tao School of Biomedical Engineering - Southern Medical University - Guangzhou - Guangdong, China , Ding, Lei Stephenson School of Biomedical Engineering - University of Oklahoma - Norman, USA , Tan, Xiaodan School of Biomedical Engineering - Southern Medical University - Guangzhou - Guangdong, China

  • Pages
    9
  • From page
    1
  • To page
    9
  • Abstract
    The use of maximum length sequence (m-sequence) has been found beneficial for recovering both linear and nonlinear components at rapid stimulation. Since m-sequence is fully characterized by a primitive polynomial of different orders, the selection of polynomial order can be problematic in practice. Usually, the m-sequence is repetitively delivered in a looped fashion. Ensemble averaging is carried out as the first step and followed by the cross-correlation analysis to deconvolve linear/nonlinear responses. According to the classical noise reduction property based on additive noise model, theoretical equations have been derived in measuring noise attenuation ratios (NARs) after the averaging and correlation processes in the present study. A computer simulation experiment was conducted to test the derived equations, and a nonlinear deconvolution experiment was also conducted using order 7 and 9 m-sequences to address this issue with real data. Both theoretical and experimental results show that the NAR is essentially independent of the m-sequence order and is decided by the total length of valid data, as well as stimulation rate. The present study offers a guideline for m-sequence selections, which can be used to estimate required recording time and signal-to-noise ratio in designing m-sequence experiments.
  • Keywords
    Evoked , Usually , NARs , signal-to-noise
  • Journal title
    Computational and Mathematical Methods in Medicine
  • Serial Year
    2017
  • Record number

    2609849