Estimation of evoked potentials using high order statistics-based adaptive filter

Bor-Shyh Lin; Bor Shing Lin; Shu Mei Wu; Jen Chien Chien; Fok Ching Chong

doi:10.1109/IEMBS.2001.1020647

Estimation of evoked potentials using high order statistics-based adaptive filter

Bor-Shyh Lin, Bor Shing Lin, Shu Mei Wu, Jen Chien Chien, Fok Ching Chong

Institute of Imaging and Biomedical Photonics

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

This paper is to present a high order statistics-based adaptive interference cancel filter (AIC-HOS) to process evoked potential (EP). In conventional ensemble averaging method, experiments have to conduct repetitively to record the required data. In normalized LMS adaptive filter, inappropriate step size always causes deficiency. This AIC-HOS system has none of the above disadvantages. This system was experimented in somatosensory evoked potential corrupted with EEG. Gradient type algorithm is used in this AIC-HOS structure to regulate the SNR of EEG and EP. This method is also simulated with visual evoked potential and audio evoked potential. The results obtained are satisfactory and acceptable in clinical usage. The AIC-HOS is superior to normalized LMS using adaptive filter in that it converges easily. Moreover, it is not sensitive to selection of step size in stabilities in convergency.

Original language	English
Pages (from-to)	2094-2096
Number of pages	3
Journal	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume	2
DOIs	https://doi.org/10.1109/IEMBS.2001.1020647
State	Published - 1 Dec 2001
Event	23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Istanbul, Turkey Duration: 25 Oct 2001 → 28 Oct 2001

Keywords

Adaptive filter
Evoked potential
High order statistics

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10.1109/IEMBS.2001.1020647

Cite this

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title = "Estimation of evoked potentials using high order statistics-based adaptive filter",

abstract = "This paper is to present a high order statistics-based adaptive interference cancel filter (AIC-HOS) to process evoked potential (EP). In conventional ensemble averaging method, experiments have to conduct repetitively to record the required data. In normalized LMS adaptive filter, inappropriate step size always causes deficiency. This AIC-HOS system has none of the above disadvantages. This system was experimented in somatosensory evoked potential corrupted with EEG. Gradient type algorithm is used in this AIC-HOS structure to regulate the SNR of EEG and EP. This method is also simulated with visual evoked potential and audio evoked potential. The results obtained are satisfactory and acceptable in clinical usage. The AIC-HOS is superior to normalized LMS using adaptive filter in that it converges easily. Moreover, it is not sensitive to selection of step size in stabilities in convergency.",

keywords = "Adaptive filter, Evoked potential, High order statistics",

author = "Bor-Shyh Lin and Lin, {Bor Shing} and Wu, {Shu Mei} and Chien, {Jen Chien} and Chong, {Fok Ching}",

year = "2001",

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doi = "10.1109/IEMBS.2001.1020647",

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T1 - Estimation of evoked potentials using high order statistics-based adaptive filter

AU - Lin, Bor-Shyh

AU - Lin, Bor Shing

AU - Wu, Shu Mei

AU - Chien, Jen Chien

AU - Chong, Fok Ching

PY - 2001/12/1

Y1 - 2001/12/1

N2 - This paper is to present a high order statistics-based adaptive interference cancel filter (AIC-HOS) to process evoked potential (EP). In conventional ensemble averaging method, experiments have to conduct repetitively to record the required data. In normalized LMS adaptive filter, inappropriate step size always causes deficiency. This AIC-HOS system has none of the above disadvantages. This system was experimented in somatosensory evoked potential corrupted with EEG. Gradient type algorithm is used in this AIC-HOS structure to regulate the SNR of EEG and EP. This method is also simulated with visual evoked potential and audio evoked potential. The results obtained are satisfactory and acceptable in clinical usage. The AIC-HOS is superior to normalized LMS using adaptive filter in that it converges easily. Moreover, it is not sensitive to selection of step size in stabilities in convergency.

AB - This paper is to present a high order statistics-based adaptive interference cancel filter (AIC-HOS) to process evoked potential (EP). In conventional ensemble averaging method, experiments have to conduct repetitively to record the required data. In normalized LMS adaptive filter, inappropriate step size always causes deficiency. This AIC-HOS system has none of the above disadvantages. This system was experimented in somatosensory evoked potential corrupted with EEG. Gradient type algorithm is used in this AIC-HOS structure to regulate the SNR of EEG and EP. This method is also simulated with visual evoked potential and audio evoked potential. The results obtained are satisfactory and acceptable in clinical usage. The AIC-HOS is superior to normalized LMS using adaptive filter in that it converges easily. Moreover, it is not sensitive to selection of step size in stabilities in convergency.

KW - Adaptive filter

KW - Evoked potential

KW - High order statistics

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T2 - 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 25 October 2001 through 28 October 2001

ER -

Estimation of evoked potentials using high order statistics-based adaptive filter

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Keywords

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