Wavelet-based denoising for EEG-based pattern recognition systems

Binh Nguyen, Wanli Ma, Dat Tran, Younjin Chung

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

Electroencephalogram (EEG) has been widely studied for EEG-based pattern recognition systems such as seizure, sleep stage, emotion, alcoholics and person recognitions. However, EEG signals are subject to noise and artifacts, which negatively affects to the pattern recognition systems. Hence, an effective EEG denoising technique is becoming necessary. In this paper, we propose an EEG denoising technique in which noisy signals are decomposed by a Wavelet transform operation, followed by Thresholding component using Energy Packing Efficiency, before being reconstructed to obtain the clean signals. The experiments are conducted on two EEG public datasets and the results show that our proposed technique achieves good performance on denoising EEG signals and improves EEG-based pattern recognition systems the most.

Original languageEnglish
Title of host publication2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
EditorsHussein Abbass, Carlos A. Coello Coello, Hemant Kumar Singh
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1249-1256
Number of pages8
ISBN (Electronic)9781728125473
ISBN (Print)9781728125480
DOIs
Publication statusPublished - 1 Dec 2020
Externally publishedYes
Event2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 - Virtual, Canberra, Australia
Duration: 1 Dec 20204 Dec 2020

Publication series

Name2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020

Conference

Conference2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
CountryAustralia
CityVirtual, Canberra
Period1/12/204/12/20

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