The Optimal Wavelet Basis for Electroencephalogram Denoising
Pages 85 - 91
Abstract
To solve the problem of optimal wavelet basis selection in motor imagery electroencephalogram (MI-EEG) denoising by wavelet transform, based on the analysis of wavelet basis parameters and characteristics, combined with the characteristics of MI-EEG, we summarized the characteristics of wavelet basis suitable for MI-EEG denoising. Signal to noise ratio (SNR) and root mean squared error (RMSE) are introduced as evaluation criteria of signal denoising effect, it is concluded that the bior and rbio wavelet basis functions are better at denoising MI-EEG among the 7 types of wavelet clusters. Among them, the rbio2.2 wavelet basis is the most suitable for MI-EEG denoising. The comparison of simulation results verifies the correctness of the conclusions.
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Index Terms
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Published In
September 2020
313 pages
ISBN:9781450388603
DOI:10.1145/3429889
Copyright © 2020 ACM.
© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.
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Association for Computing Machinery
New York, NY, United States
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Published: 04 December 2020
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- Refereed limited
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- Natural Science Basic Research Program of Shaanxi
- National Key R&D Program of China
- National Natural Science Foundation of China
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ISAIMS 2020
ISAIMS 2020: 2020 International Symposium on Artificial Intelligence in Medical Sciences
September 11 - 13, 2020
Beijing, China
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ISAIMS '20 Paper Acceptance Rate 53 of 112 submissions, 47%;
Overall Acceptance Rate 53 of 112 submissions, 47%
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