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
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