An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT
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 (elec...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/7/1664 |
| _version_ | 1818016009307553792 |
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| author | Haiping Huang Linkang Hu Fu Xiao Anming Du Ning Ye Fan He |
| author_facet | Haiping Huang Linkang Hu Fu Xiao Anming Du Ning Ye Fan He |
| author_sort | Haiping Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T07:05:55Z |
| format | Article |
| id | doaj.art-8f43c2ebb6c54ad78dd2dc902d01b102 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T07:05:55Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8f43c2ebb6c54ad78dd2dc902d01b1022022-12-22T02:06:34ZengMDPI AGSensors1424-82202019-04-01197166410.3390/s19071664s19071664An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoTHaiping Huang0Linkang Hu1Fu Xiao2Anming Du3Ning Ye4Fan He5College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaWith 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.https://www.mdpi.com/1424-8220/19/7/1664EEGIoTbrainwavesidentity authenticationaudiovisual paradigmbagging ensemble learning |
| spellingShingle | Haiping Huang Linkang Hu Fu Xiao Anming Du Ning Ye Fan He An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT Sensors EEG IoT brainwaves identity authentication audiovisual paradigm bagging ensemble learning |
| title | An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT |
| title_full | An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT |
| title_fullStr | An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT |
| title_full_unstemmed | An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT |
| title_short | An EEG-Based Identity Authentication System with Audiovisual Paradigm in IoT |
| title_sort | eeg based identity authentication system with audiovisual paradigm in iot |
| topic | EEG IoT brainwaves identity authentication audiovisual paradigm bagging ensemble learning |
| url | https://www.mdpi.com/1424-8220/19/7/1664 |
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