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. 2019 Jun 2:2019:7895924.
doi: 10.1155/2019/7895924. eCollection 2019.

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification

Chi Qin Lai  1 , Haidi Ibrahim  1 , Mohd Zaid Abdullah  1 , Jafri Malin Abdullah  2 , Shahrel Azmin Suandi  1 , Azlinda Azman  3
Affiliations

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification

Chi Qin Lai et al. Comput Intell Neurosci. .

Abstract

Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.

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Figures

Figure 1
Figure 1
General framework of a biometric authentication system.
Figure 2
Figure 2
Example of the CNN structure.
Figure 3
Figure 3
Structure of the CNN proposed by Ma et al. [34].
Figure 4
Figure 4
Default arrangement of EEG channels.
Figure 5
Figure 5
Matrix M1.
Figure 6
Figure 6
Matrix M2.
Figure 7
Figure 7
Matrix M3.
Figure 8
Figure 8
Image I1.
Figure 9
Figure 9
Image I2.
Figure 10
Figure 10
Image I3.
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