Eye-blink artifact removal from single channel EEG with k-means and SSA

Abstract In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels...

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Autores principales:	Ajay Kumar Maddirala, Kalyana C Veluvolu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8decc1769c49457c881365fe1042e14e
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spelling	oai:doaj.org-article:8decc1769c49457c881365fe1042e14e2021-12-02T15:49:35ZEye-blink artifact removal from single channel EEG with k-means and SSA10.1038/s41598-021-90437-72045-2322https://doaj.org/article/8decc1769c49457c881365fe1042e14e2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90437-7https://doaj.org/toc/2045-2322Abstract In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ( $$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.Ajay Kumar MaddiralaKalyana C VeluvoluNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ajay Kumar Maddirala Kalyana C Veluvolu Eye-blink artifact removal from single channel EEG with k-means and SSA
description	Abstract In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ( $$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.
format	article
author	Ajay Kumar Maddirala Kalyana C Veluvolu
author_facet	Ajay Kumar Maddirala Kalyana C Veluvolu
author_sort	Ajay Kumar Maddirala
title	Eye-blink artifact removal from single channel EEG with k-means and SSA
title_short	Eye-blink artifact removal from single channel EEG with k-means and SSA
title_full	Eye-blink artifact removal from single channel EEG with k-means and SSA
title_fullStr	Eye-blink artifact removal from single channel EEG with k-means and SSA
title_full_unstemmed	Eye-blink artifact removal from single channel EEG with k-means and SSA
title_sort	eye-blink artifact removal from single channel eeg with k-means and ssa
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8decc1769c49457c881365fe1042e14e
work_keys_str_mv	AT ajaykumarmaddirala eyeblinkartifactremovalfromsinglechanneleegwithkmeansandssa AT kalyanacveluvolu eyeblinkartifactremovalfromsinglechanneleegwithkmeansandssa
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Eye-blink artifact removal from single channel EEG with k-means and SSA

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