research-article

Wavelet-based envelope features with automatic EOG artifact removal

Authors:

I-Ru ChenAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 39, Issue 3

Pages 2743 - 2749

https://doi.org/10.1016/j.eswa.2011.08.132

Published: 01 February 2012 Publication History

Abstract

Highlights Automatic EOG artifact removal is proposed to eliminate the EOG artifacts by means of ICA and correlation coefficient. The features are extracted from wavelet transform data by amplitude modulation method. The SVM is used for the discrimination of wavelet-based AM features. The proposed method compares with EEG data without EOG artifact removal, band power features and LDA classifier. In this study, we propose an analysis system for single-trial classification of electroencephalogram (EEG) data. Combined with automatic EOG artifact removal and wavelet-based amplitude modulation (AM) features, the support vector machine (SVM) classifier is applied to the classification of left finger lifting and resting. Automatic EOG artifact removal is proposed to eliminate the EOG artifacts automatically by means of independent component analysis (ICA) and correlation coefficient. The features are then extracted from the discrete wavelet transform (DWT) data by the AM method. Finally, the SVM is used for the discriminant of wavelet-based AM features. Compared with EEG data without EOG artifact removal, band power features and LDA classifier, the proposed system achieves promising results in classification accuracy.

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Cited By

Reddy GProulx MHirshfield LRies A(2024)Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XRProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641925(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641925
Sharma PRaja RVishwakarma SSharma SMishra PKushwah V(2022)RETRACTED ARTICLE: Analysis of brain signal processing and real-time EEG signal enhancementMultimedia Tools and Applications10.1007/s11042-022-12887-z81:28(41013-41033)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-12887-z
Shukla PRoy VShukla PGupta AGoel VShukla S(2021)Taxonomy on EEG Artifacts Removal Methods, Issues, and Healthcare ApplicationsJournal of Organizational and End User Computing10.4018/JOEUC.202101010233:1(19-46)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.4018/JOEUC.2021010102
Show More Cited By

Wavelet-based envelope features with automatic EOG artifact removal
1. Computing methodologies
  1. Machine learning
2. Hardware
  1. Communication hardware, interfaces and storage

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 39, Issue 3

February, 2012

1661 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2012

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Cited By

Reddy GProulx MHirshfield LRies A(2024)Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XRProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641925(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641925
Sharma PRaja RVishwakarma SSharma SMishra PKushwah V(2022)RETRACTED ARTICLE: Analysis of brain signal processing and real-time EEG signal enhancementMultimedia Tools and Applications10.1007/s11042-022-12887-z81:28(41013-41033)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-12887-z
Shukla PRoy VShukla PGupta AGoel VShukla S(2021)Taxonomy on EEG Artifacts Removal Methods, Issues, and Healthcare ApplicationsJournal of Organizational and End User Computing10.4018/JOEUC.202101010233:1(19-46)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.4018/JOEUC.2021010102
Liu RZhang ZDuan FZhou XMeng Z(2017)Identification of Anisomerous Motor Imagery EEG Signals Based on Complex AlgorithmsComputational Intelligence and Neuroscience10.1155/2017/27278562017Online publication date: 1-Jan-2017
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Bigirimana ASiddique NCoyle D(2016)A hybrid ICA-wavelet transform for automated artefact removal in EEG-based emotion recognition2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2016.7844928(004429-004434)Online publication date: 9-Oct-2016
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Asensio-Cubero JGan JPalaniappan R(2016)Multiresolution analysis over graphs for a motor imagery based online BCI gameComputers in Biology and Medicine10.1016/j.compbiomed.2015.10.01668:C(21-26)Online publication date: 1-Jan-2016
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Wu CTzeng YKuo BTzeng G(2014)Integration of affective computing techniques and soft computing for developing a human affective recognition system for U-learning systemsInternational Journal of Mobile Learning and Organisation10.1504/IJMLO.2014.0599978:1(50-66)Online publication date: 1-Mar-2014
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Fernández-Blanco ERivero DGestal MDorado J(2013)Classification of signals by means of Genetic ProgrammingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-013-1036-417:10(1929-1937)Online publication date: 1-Oct-2013
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Chu HLiau CLin CSu B(2012)Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan regionExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.02.11439:10(9451-9457)Online publication date: 1-Aug-2012
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