Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram

The interference of eye blink artifacts can cause serious distortion to electroencephalogram (EEG) which could bias the signal interpretation and reduce the classification accuracy in a brain-computer interface (BCI) application. To overcome this problem, an algorithm to automatically detect and rem...

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Main Authors:	Abd Rahman, Faridah, Othman, Mohd Fauzi, Hamzah, Mohd Ilham Rusydan Hamzah
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2018
Subjects:	TK Electrical engineering. Electronics Nuclear engineering TK5101 Telecommunication. Including telegraphy, radio, radar, television TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/70296/ http://irep.iium.edu.my/70296/ http://irep.iium.edu.my/70296/ http://irep.iium.edu.my/70296/7/70296_Comparison%20on%20performance%20of%20adaptive%20algorithms_SCOPUS.pdf http://irep.iium.edu.my/70296/19/70296_Comparison%20on%20performance%20of%20adaptive.pdf

id	iium-70296
recordtype	eprints
spelling	iium-702962019-02-19T08:30:20Z http://irep.iium.edu.my/70296/ Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram Abd Rahman, Faridah Othman, Mohd Fauzi Hamzah, Mohd Ilham Rusydan Hamzah TK Electrical engineering. Electronics Nuclear engineering TK5101 Telecommunication. Including telegraphy, radio, radar, television TK7885 Computer engineering The interference of eye blink artifacts can cause serious distortion to electroencephalogram (EEG) which could bias the signal interpretation and reduce the classification accuracy in a brain-computer interface (BCI) application. To overcome this problem, an algorithm to automatically detect and remove the artifacts from EEG signals is highly desirable. One of the methods that can be applied for automatic artifacts removal is adaptive filtering through an adaptive noise cancellation (ANC) system. In this paper, we compare the performance of three adaptive algorithms; namely LMS, RLS, and ANFIS, in removing the eye blink from EEG signals. To evaluate the results, the SNR, MSE and correlation coefficient values are calculated based on the results obtained by using one of the widely used methods for blinks removal, independent component analysis (ICA). The results show that RLS algorithm provides the best performance when comparing with the ICA method. Institute of Electrical and Electronics Engineers Inc. 2018 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/70296/7/70296_Comparison%20on%20performance%20of%20adaptive%20algorithms_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/70296/19/70296_Comparison%20on%20performance%20of%20adaptive.pdf Abd Rahman, Faridah and Othman, Mohd Fauzi and Hamzah, Mohd Ilham Rusydan Hamzah (2018) Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram. In: International Conference on Computer and Communication Engineering (ICCCE), 19th-20th September 2018, Kuala Lumpur. https://ieeexplore.ieee.org/document/8539290 10.1109/ICCCE.2018.8539290
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering TK5101 Telecommunication. Including telegraphy, radio, radar, television TK7885 Computer engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering TK5101 Telecommunication. Including telegraphy, radio, radar, television TK7885 Computer engineering Abd Rahman, Faridah Othman, Mohd Fauzi Hamzah, Mohd Ilham Rusydan Hamzah Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
description	The interference of eye blink artifacts can cause serious distortion to electroencephalogram (EEG) which could bias the signal interpretation and reduce the classification accuracy in a brain-computer interface (BCI) application. To overcome this problem, an algorithm to automatically detect and remove the artifacts from EEG signals is highly desirable. One of the methods that can be applied for automatic artifacts removal is adaptive filtering through an adaptive noise cancellation (ANC) system. In this paper, we compare the performance of three adaptive algorithms; namely LMS, RLS, and ANFIS, in removing the eye blink from EEG signals. To evaluate the results, the SNR, MSE and correlation coefficient values are calculated based on the results obtained by using one of the widely used methods for blinks removal, independent component analysis (ICA). The results show that RLS algorithm provides the best performance when comparing with the ICA method.
format	Conference or Workshop Item
author	Abd Rahman, Faridah Othman, Mohd Fauzi Hamzah, Mohd Ilham Rusydan Hamzah
author_facet	Abd Rahman, Faridah Othman, Mohd Fauzi Hamzah, Mohd Ilham Rusydan Hamzah
author_sort	Abd Rahman, Faridah
title	Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
title_short	Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
title_full	Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
title_fullStr	Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
title_full_unstemmed	Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
title_sort	comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2018
url	http://irep.iium.edu.my/70296/ http://irep.iium.edu.my/70296/ http://irep.iium.edu.my/70296/ http://irep.iium.edu.my/70296/7/70296_Comparison%20on%20performance%20of%20adaptive%20algorithms_SCOPUS.pdf http://irep.iium.edu.my/70296/19/70296_Comparison%20on%20performance%20of%20adaptive.pdf
first_indexed	2023-09-18T21:39:48Z
last_indexed	2023-09-18T21:39:48Z
_version_	1777413063905902592

Comparison on performance of adaptive algorithms for eye blinks removal in electroencephalogram

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