An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods

<p>In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular...

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Main Authors: Arjon Turnip, Iwan R. Setiawan, Edy Junaidi, Le Hoa Nguyen
Format: Article
Language:English
Published: Indonesian Institute of Sciences 2014-12-01
Series:Journal of Mechatronics, Electrical Power, and Vehicular Technology
Subjects:
Online Access:http://mevjournal.com/index.php/mev/article/view/207
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spelling doaj-0a1cc25df34d42f882df79e51c61353a2020-11-25T03:43:01ZengIndonesian Institute of SciencesJournal of Mechatronics, Electrical Power, and Vehicular Technology2087-33792088-69852014-12-015212913810.14203/j.mev.2014.v5.129-138109An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA MethodsArjon Turnip0Iwan R. Setiawan1Edy JunaidiLe Hoa Nguyen2Technical Implementation Unit for Instrumentation Development, Indonesian Institute of SciencesTechnical Implementation Unit for Instrumentation Development, Indonesian Institute of SciencesDept. of Electrical Engineering, University of Science and Technology-The University of Danang<p>In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained.</p><p> </p>http://mevjournal.com/index.php/mev/article/view/207EEG, EOG, ICA, PCA, artifacts elimination
collection DOAJ
language English
format Article
sources DOAJ
author Arjon Turnip
Iwan R. Setiawan
Edy Junaidi
Le Hoa Nguyen
spellingShingle Arjon Turnip
Iwan R. Setiawan
Edy Junaidi
Le Hoa Nguyen
An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods
Journal of Mechatronics, Electrical Power, and Vehicular Technology
EEG, EOG, ICA, PCA, artifacts elimination
author_facet Arjon Turnip
Iwan R. Setiawan
Edy Junaidi
Le Hoa Nguyen
author_sort Arjon Turnip
title An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods
title_short An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods
title_full An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods
title_fullStr An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods
title_full_unstemmed An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods
title_sort experiment of ocular artifacts elimination from eeg signals using ica and pca methods
publisher Indonesian Institute of Sciences
series Journal of Mechatronics, Electrical Power, and Vehicular Technology
issn 2087-3379
2088-6985
publishDate 2014-12-01
description <p>In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained.</p><p> </p>
topic EEG, EOG, ICA, PCA, artifacts elimination
url http://mevjournal.com/index.php/mev/article/view/207
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