EEG Based Biometric Identification Using Correlation and MLPNN Models

Authors

  • T Waili Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Selangor, Malaysia
  • Md Gapar Md Johar Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Selangor, Malaysia
  • K. A. Sidek Department of Electrical and Computer Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • N. S. H. Mohd Nor Department of Electrical and Computer Engineering, Kulliyyah of Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • H. Yaacob Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • M. Othman Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.3991/ijoe.v15i10.10880

Keywords:

Biometric, EEG, Correlation, MLPNN

Abstract


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 cross-correlation 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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Published

2019-06-27

How to Cite

Waili, T., Johar, M. G. M., Sidek, K. A., Nor, N. S. H. M., Yaacob, H., & Othman, M. (2019). EEG Based Biometric Identification Using Correlation and MLPNN Models. International Journal of Online and Biomedical Engineering (iJOE), 15(10), pp. 77–90. https://doi.org/10.3991/ijoe.v15i10.10880

Issue

Vol. 15 No. 10 (2019)

Section

Papers

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