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Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform

At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet tra...

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Autores principales: Wang, Xian-Yu, Li, Cong, Zhang, Rui, Wang, Liang, Tan, Jin-Lin, Wang, Hai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198366/
https://www.ncbi.nlm.nih.gov/pubmed/35720691
http://dx.doi.org/10.3389/fnins.2022.921642
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author Wang, Xian-Yu
Li, Cong
Zhang, Rui
Wang, Liang
Tan, Jin-Lin
Wang, Hai
author_facet Wang, Xian-Yu
Li, Cong
Zhang, Rui
Wang, Liang
Tan, Jin-Lin
Wang, Hai
author_sort Wang, Xian-Yu
collection PubMed
description At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet transform (WT) can provide a time-frequency representation of a dynamic process, and it has been widely utilized in salient feature analysis of EEG. In this paper, we investigate the problem of translation variability (TV) in discrete wavelet transform (DWT), which causes degradation of time-frequency localization. It will be verified through numerical simulations that TV is caused by downsampling operations in decomposition process of DWT. The presence of TV may cause severe distortions of features in wavelet subspaces. However, this phenomenon has not attracted much attention in the scientific community. Redundant discrete wavelet transform (RDWT) is derived by eliminating the downsampling operation. RDWT enjoys the attractive merit of translation invariance. RDWT shares the same time-frequency pattern with that of DWT. The discrete delta impulse function is used to test the time-frequency response of DWT and RDWT in wavelet subspaces. The results show that DWT is very sensitive to the translation of delta impulse function, while RDWT keeps the decomposition results unchanged. This conclusion has also been verified again in decomposition of actual EEG signals. In conclusion, to avoid possible distortions of features caused by translation sensitivity in DWT, we recommend the use of RDWT with more stable performance in BCI research and clinical applications.
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spelling pubmed-91983662022-06-16 Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform Wang, Xian-Yu Li, Cong Zhang, Rui Wang, Liang Tan, Jin-Lin Wang, Hai Front Neurosci Neuroscience At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet transform (WT) can provide a time-frequency representation of a dynamic process, and it has been widely utilized in salient feature analysis of EEG. In this paper, we investigate the problem of translation variability (TV) in discrete wavelet transform (DWT), which causes degradation of time-frequency localization. It will be verified through numerical simulations that TV is caused by downsampling operations in decomposition process of DWT. The presence of TV may cause severe distortions of features in wavelet subspaces. However, this phenomenon has not attracted much attention in the scientific community. Redundant discrete wavelet transform (RDWT) is derived by eliminating the downsampling operation. RDWT enjoys the attractive merit of translation invariance. RDWT shares the same time-frequency pattern with that of DWT. The discrete delta impulse function is used to test the time-frequency response of DWT and RDWT in wavelet subspaces. The results show that DWT is very sensitive to the translation of delta impulse function, while RDWT keeps the decomposition results unchanged. This conclusion has also been verified again in decomposition of actual EEG signals. In conclusion, to avoid possible distortions of features caused by translation sensitivity in DWT, we recommend the use of RDWT with more stable performance in BCI research and clinical applications. Frontiers Media S.A. 2022-06-01 /pmc/articles/PMC9198366/ /pubmed/35720691 http://dx.doi.org/10.3389/fnins.2022.921642 Text en Copyright © 2022 Wang, Li, Zhang, Wang, Tan and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Wang, Xian-Yu
Li, Cong
Zhang, Rui
Wang, Liang
Tan, Jin-Lin
Wang, Hai
Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform
title Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform
title_full Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform
title_fullStr Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform
title_full_unstemmed Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform
title_short Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform
title_sort intelligent extraction of salient feature from electroencephalogram using redundant discrete wavelet transform
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198366/
https://www.ncbi.nlm.nih.gov/pubmed/35720691
http://dx.doi.org/10.3389/fnins.2022.921642
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