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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9198366 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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