Hybrid Model for Motor Imagery Biometric Identification

Abstract

Biometric systems are a continuously evolving and promising technological domain that can be used in automatic systems for the unique and efficient identification and authentication of individuals without necessitating users to carry or remember any physical tokens or passwords, in contrast to traditional methods such as password IDs. Biometrics are biological measurements or physical characteristics that can be used to ascertain and validate the identity of individuals. Recently, considerable interest has emerged in exploiting brain activity as a biometric identifier in automatic recognition systems, particularly focusing on data acquired through electroencephalography (EEG). Multiple research endeavors have indeed confirmed the presence of discriminative characteristics within brain signals recorded while performing specific cognitive tasks. However, EEG signals are inherently complex due to their nonstationary and high-dimensional properties, thus demanding careful consideration during both the feature extraction and classification processes. This study applied a hybridization technique integrating a pre-trained convolutional neural network (CNN) with a classical classifier and the short-time Fourier transform (STFT) spectrum. We used a hybrid model to decode two-class motor imagery (MI) signals for mobile biometric authentication tasks, which include subject identification and lock and unlock classification. To this purpose, nine potential classifiers (mostly classification algorithms) were utilized to build nine distinct hybrid models, with the ultimate goal of selecting the most effective one. Practically, six experiments were conducted in the experimental part of this study. The first experiment aims to develop a hybrid model for biometric authentication tasks. To do this, nine possible classifiers (mostly classification algorithms) were used to build nine hybrid models. It can be seen that the RF-VGG model achieved better performance compared with other models. Therefore, it was chosen to be utilized for mobile biometric authentication. The fourth experiment is to apply the RF-VGG model for doing the lock and unlock classification process, and their mean accuracy is 97.50%. Consequently, the fifth experiment was conducted to validate the RF-VGG model for the lock and unlock task, and their mean accuracy was 97.40%. Practically, the sixth experiment was to verify the RF-VGG model for the lock and unlock task over another dataset (unseen data), and their accuracy is 94.4%. It can be deduced that the hybrid model appraises the capability of decoding the MI signal for the left and right hand. Therefore, the RF-VGG model can contribute to the BCI-MI community by facilitating the deployment of the mobile biometric authentication task for (the subject identification and the lock and unlock classification).

Keywords

• Deep Learning
• brain computer interface
• MI
• Motor Imagery
• Transfer Learning
• Biometrics