Transform domain adaptive enhancement filter for evoked potential

Bor-Shyh Lin; Bor Shing Lin; Jen Chien Chien; Fok Ching Chong; Yue Der Lin

doi:10.1109/IEMBS.2002.1134491

Transform domain adaptive enhancement filter for evoked potential

Bor-Shyh Lin, Bor Shing Lin, Jen Chien Chien, Fok Ching Chong, Yue Der Lin

影像與生醫光電研究所

研究成果: Conference article › 同行評審

1 引文斯高帕斯（Scopus）

摘要

This paper is to present the use of transform domain adaptive filter (TDADF) to process evoked potential (EP). The use of adaptive filter (ADF) in processing EP is effective than traditional averaging method. However to design an ADF with normalized LMS with good convergence performance is not an easy task. Thus, we proposed the TDADF method to improve the convergence performance of processing EP. Simulations were conducted to obtain the SNR of visual evoked potential. Moreover, collected data from somatosensory evoked potential was used in the testing. We observed that TDADF method has better performance than ADF with normalized LMS algorithm.

原文	English
頁（從 - 到）	277-278
頁數	2
期刊	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
卷	1
DOIs	https://doi.org/10.1109/IEMBS.2002.1134491
出版狀態	Published - 1 12月 2002
事件	Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, 美國持續時間: 23 10月 2002 → 26 10月 2002

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10.1109/IEMBS.2002.1134491

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@article{db55ed8051f8400bad4bbd78ba49e709,

title = "Transform domain adaptive enhancement filter for evoked potential",

abstract = "This paper is to present the use of transform domain adaptive filter (TDADF) to process evoked potential (EP). The use of adaptive filter (ADF) in processing EP is effective than traditional averaging method. However to design an ADF with normalized LMS with good convergence performance is not an easy task. Thus, we proposed the TDADF method to improve the convergence performance of processing EP. Simulations were conducted to obtain the SNR of visual evoked potential. Moreover, collected data from somatosensory evoked potential was used in the testing. We observed that TDADF method has better performance than ADF with normalized LMS algorithm.",

keywords = "Evoked potential, Transform domain adaptive filter",

author = "Bor-Shyh Lin and Lin, {Bor Shing} and Chien, {Jen Chien} and Chong, {Fok Ching} and Lin, {Yue Der}",

year = "2002",

month = dec,

day = "1",

doi = "10.1109/IEMBS.2002.1134491",

language = "English",

volume = "1",

pages = "277--278",

journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",

issn = "0589-1019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) ; Conference date: 23-10-2002 Through 26-10-2002",

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TY - JOUR

T1 - Transform domain adaptive enhancement filter for evoked potential

AU - Lin, Bor-Shyh

AU - Lin, Bor Shing

AU - Chien, Jen Chien

AU - Chong, Fok Ching

AU - Lin, Yue Der

PY - 2002/12/1

Y1 - 2002/12/1

N2 - This paper is to present the use of transform domain adaptive filter (TDADF) to process evoked potential (EP). The use of adaptive filter (ADF) in processing EP is effective than traditional averaging method. However to design an ADF with normalized LMS with good convergence performance is not an easy task. Thus, we proposed the TDADF method to improve the convergence performance of processing EP. Simulations were conducted to obtain the SNR of visual evoked potential. Moreover, collected data from somatosensory evoked potential was used in the testing. We observed that TDADF method has better performance than ADF with normalized LMS algorithm.

AB - This paper is to present the use of transform domain adaptive filter (TDADF) to process evoked potential (EP). The use of adaptive filter (ADF) in processing EP is effective than traditional averaging method. However to design an ADF with normalized LMS with good convergence performance is not an easy task. Thus, we proposed the TDADF method to improve the convergence performance of processing EP. Simulations were conducted to obtain the SNR of visual evoked potential. Moreover, collected data from somatosensory evoked potential was used in the testing. We observed that TDADF method has better performance than ADF with normalized LMS algorithm.

KW - Evoked potential

KW - Transform domain adaptive filter

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U2 - 10.1109/IEMBS.2002.1134491

DO - 10.1109/IEMBS.2002.1134491

M3 - Conference article

AN - SCOPUS:0036907766

SN - 0589-1019

VL - 1

SP - 277

EP - 278

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

T2 - Proceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS)

Y2 - 23 October 2002 through 26 October 2002

ER -

Transform domain adaptive enhancement filter for evoked potential

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