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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Kassel University Press GmbH
2019
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| Subjects: | |
| 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 |
| Summary: | 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. |
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