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EEG based biometric identification using correlation and MLPNN models

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

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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.

Item Type: Article (Journal)
Additional Information: 4698/73319
Uncontrolled Keywords: Biometric, EEG, Correlation and MLPNN
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Assoc Prof Dr Khairul Azami Sidek
Date Deposited: 19 Jul 2019 16:17
Last Modified: 25 Mar 2020 21:27
URI: http://irep.iium.edu.my/id/eprint/73319

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