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Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. T...

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Detalles Bibliográficos
Autores principales: Bang, Jae Won, Choi, Jong-Suk, Park, Kang Ryoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3690055/
https://www.ncbi.nlm.nih.gov/pubmed/23669713
http://dx.doi.org/10.3390/s130506272
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author Bang, Jae Won
Choi, Jong-Suk
Park, Kang Ryoung
author_facet Bang, Jae Won
Choi, Jong-Suk
Park, Kang Ryoung
author_sort Bang, Jae Won
collection PubMed
description Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.
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spelling pubmed-36900552013-07-09 Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images Bang, Jae Won Choi, Jong-Suk Park, Kang Ryoung Sensors (Basel) Article Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods. Molecular Diversity Preservation International (MDPI) 2013-05-13 /pmc/articles/PMC3690055/ /pubmed/23669713 http://dx.doi.org/10.3390/s130506272 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Bang, Jae Won
Choi, Jong-Suk
Park, Kang Ryoung
Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images
title Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images
title_full Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images
title_fullStr Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images
title_full_unstemmed Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images
title_short Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images
title_sort noise reduction in brainwaves by using both eeg signals and frontal viewing camera images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3690055/
https://www.ncbi.nlm.nih.gov/pubmed/23669713
http://dx.doi.org/10.3390/s130506272
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