Abstract
With the continuous increment of security risks and the limitations of traditional modes, it is necessary to design a universal and trustworthy identity authentication system for intelligent Internet of Things (IoT) applications such as an intelligent entrance guard. The characteristics of EEG (electroencephalography) have gained the confidence of researchers due to its uniqueness, stability, and universality. However, the limited usability of the experimental paradigm and the unsatisfactory classification accuracy have so far prevented the identity authentication system based on EEG to become commonplace in IoT scenarios. To address these problems, an audiovisual presentation paradigm is proposed to record the EEG signals of subjects. In the pre-processing stage, the reference electrode, ensemble averaging, and independent component analysis methods are used to remove artifacts. In the feature extraction stage, adaptive feature selection and bagging ensemble learning algorithms establish the optimal classification model. The experimental result shows that our proposal achieves the best classification accuracy when compared with other paradigms and typical EEG-based authentication methods, and the test evaluation on a login scenario is designed to further demonstrate that the proposed system is feasible, effective, and reliable.
Highlights
The rapid development of smart sensing devices, wireless communications, and mobile intelligent computing has prompted potential applications of Internet of Things (IoT) [1,2]; security issues have always been one of the concerns of IoT applications
We evaluate the classification performance of three experimental paradigms through an analysis and comparison of the accuracy rate, precision rate, and false positive for each subject, and we verify the superiority of the audiovisual paradigm
The subjects perform the visual and auditory paradigm in a open environment due to the constrains of experimental conditions, so there are many uncertainties in the test process: (1) the mental state of the subject may be bad; (2) the subject cannot correctly follow the authentication instructions of the experimental paradigm; (3) improperly wearing the EEG device can cause inaccuracies in the data acquisition; or (4) the surrounding noises may interfere with the subject
Summary
The rapid development of smart sensing devices, wireless communications, and mobile intelligent computing has prompted potential applications of Internet of Things (IoT) [1,2]; security issues have always been one of the concerns of IoT applications. Intelligent access control and identity authentication, which benefit from the close technological convergence of novel IoT terminals and machine learning, have emerged as indispensable components in some IoT applications such as the smart home, intelligent building, or safeguards for the purpose of security enhancement [3,4]. In order to achieve such an intelligent identity authentication, novel IoT devices and machine learning methods will be employed, which can facilitate intelligently sensing and processing data or signals collected from the environment. In traditional IoT applications, the prevalent methods for identity authentication include user passwords, device PINs, and RF cards. These authentication methods face the risks of being stolen, lost, or forgotten after passwords are updated.
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