EEG based biometric identification using correlation and MLPNN models

This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals...

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Main Authors:	Waili, Tuerxun, Md Johar, Md Gapar, Sidek, Khairul Azami, Mohd Nor, Nur Syarmimi Hanis, Yaacob, Hamwira Sakti, Othman, Marini
Format:	Article
Language:	English English
Published:	Kassel University Press GmbH 2019
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/73319/ http://irep.iium.edu.my/73319/ http://irep.iium.edu.my/73319/ http://irep.iium.edu.my/73319/1/10880-34278-1-PB.pdf http://irep.iium.edu.my/73319/7/73319_EEG%20Based%20Biometric%20Identification%20Using%20Correlation%20and%20MLPNN%20Models_WOS.pdf

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recordtype	eprints
spelling	iium-733192019-08-01T03:05:35Z http://irep.iium.edu.my/73319/ EEG based biometric identification using correlation and MLPNN models Waili, Tuerxun Md Johar, Md Gapar Sidek, Khairul Azami Mohd Nor, Nur Syarmimi Hanis Yaacob, Hamwira Sakti Othman, Marini TK7885 Computer engineering This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals obtained from our bodies is considered more secure and privacy-compliant. The EEG signals of 6 subjects were collected where the subjects were required to undergo two baseline experiments which are, eyes open (EO) and eyes closed (EC). The signals were processed using a 2nd order Butterworth filter to eliminate the unwanted noise in the signals. Then, Daubechies (db8) wavelet was applied to the signals in the feature extraction stage and from there, Power Spectral Density (PSD) of alpha and beta waves was computed. Finally, the correlation model and Multilayer Perceptron Neural Network (MLPNN) was applied to classify the EEG signals of each subject. Correlation model has yielded great significant difference of coefficient between autocorrelation and crosscorrelation where it gives the coefficient value of 1 for autocorrelation and the coefficient value of less than 0.35 for cross-correlation. On the other hand, the MLPNN model gives an accuracy of 75.8% and 71.5% for classification during EO and EC baseline condition respectively. Therefore, these results support the usability of EEG signals in biometric recognition. Kassel University Press GmbH 2019 Article PeerReviewed application/pdf en http://irep.iium.edu.my/73319/1/10880-34278-1-PB.pdf application/pdf en http://irep.iium.edu.my/73319/7/73319_EEG%20Based%20Biometric%20Identification%20Using%20Correlation%20and%20MLPNN%20Models_WOS.pdf Waili, Tuerxun and Md Johar, Md Gapar and Sidek, Khairul Azami and Mohd Nor, Nur Syarmimi Hanis and Yaacob, Hamwira Sakti and Othman, Marini (2019) EEG based biometric identification using correlation and MLPNN models. International Journal of Online and Biomedical Engineering, 15 (10). pp. 77-90. ISSN 2626-8493 https://www.online-journals.org/index.php/i-joe/article/view/10880/5763 10.3991/ijoe.v15i10.10880
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Waili, Tuerxun Md Johar, Md Gapar Sidek, Khairul Azami Mohd Nor, Nur Syarmimi Hanis Yaacob, Hamwira Sakti Othman, Marini EEG based biometric identification using correlation and MLPNN models
description	This study investigates the capability of electroencephalogram (EEG) signals to be used for biometric identification. In the context of biometric, recently, researchers have been focusing more on biomedical signals to substitute the biometric modalities that are being used nowadays as the signals obtained from our bodies is considered more secure and privacy-compliant. The EEG signals of 6 subjects were collected where the subjects were required to undergo two baseline experiments which are, eyes open (EO) and eyes closed (EC). The signals were processed using a 2nd order Butterworth filter to eliminate the unwanted noise in the signals. Then, Daubechies (db8) wavelet was applied to the signals in the feature extraction stage and from there, Power Spectral Density (PSD) of alpha and beta waves was computed. Finally, the correlation model and Multilayer Perceptron Neural Network (MLPNN) was applied to classify the EEG signals of each subject. Correlation model has yielded great significant difference of coefficient between autocorrelation and crosscorrelation where it gives the coefficient value of 1 for autocorrelation and the coefficient value of less than 0.35 for cross-correlation. On the other hand, the MLPNN model gives an accuracy of 75.8% and 71.5% for classification during EO and EC baseline condition respectively. Therefore, these results support the usability of EEG signals in biometric recognition.
format	Article
author	Waili, Tuerxun Md Johar, Md Gapar Sidek, Khairul Azami Mohd Nor, Nur Syarmimi Hanis Yaacob, Hamwira Sakti Othman, Marini
author_facet	Waili, Tuerxun Md Johar, Md Gapar Sidek, Khairul Azami Mohd Nor, Nur Syarmimi Hanis Yaacob, Hamwira Sakti Othman, Marini
author_sort	Waili, Tuerxun
title	EEG based biometric identification using correlation and MLPNN models
title_short	EEG based biometric identification using correlation and MLPNN models
title_full	EEG based biometric identification using correlation and MLPNN models
title_fullStr	EEG based biometric identification using correlation and MLPNN models
title_full_unstemmed	EEG based biometric identification using correlation and MLPNN models
title_sort	eeg based biometric identification using correlation and mlpnn models
publisher	Kassel University Press GmbH
publishDate	2019
url	http://irep.iium.edu.my/73319/ http://irep.iium.edu.my/73319/ http://irep.iium.edu.my/73319/ http://irep.iium.edu.my/73319/1/10880-34278-1-PB.pdf http://irep.iium.edu.my/73319/7/73319_EEG%20Based%20Biometric%20Identification%20Using%20Correlation%20and%20MLPNN%20Models_WOS.pdf
first_indexed	2023-09-18T21:43:56Z
last_indexed	2023-09-18T21:43:56Z
_version_	1777413324290392064

EEG based biometric identification using correlation and MLPNN models

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