Next-Generation Personal Authentication Scheme Based on EEG Signal and Deep Learning


Gi-Chul Yang, Journal of Information Processing Systems Vol. 16, No. 5, pp. 1034-1047, Oct. 2020  

10.3745/JIPS.03.0147
Keywords: Electroencephalography, information security, Machine Learning, Personal Authentication
Fulltext:

Abstract

The personal authentication technique is an essential tool in this complex and modern digital information society. Traditionally, the most general mechanism of personal authentication was using alphanumeric passwords. However, passwords that are hard to guess or to break, are often hard to remember. There are demands for a technology capable of replacing the text-based password system. Graphical passwords can be an alternative, but it is vulnerable to shoulder-surfing attacks. This paper looks through a number of recently developed graphical password systems and introduces a personal authentication system using a machine learning technique with electroencephalography (EEG) signals as a new type of personal authentication system which is easier for a person to use and more difficult for others to steal than other preexisting authentication systems.


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Cite this article
[APA Style]
Gi-Chul Yang (2020). Next-Generation Personal Authentication Scheme Based on EEG Signal and Deep Learning. Journal of Information Processing Systems, 16(5), 1034-1047. DOI: 10.3745/JIPS.03.0147.

[IEEE Style]
G. Yang, "Next-Generation Personal Authentication Scheme Based on EEG Signal and Deep Learning," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1034-1047, 2020. DOI: 10.3745/JIPS.03.0147.

[ACM Style]
Gi-Chul Yang. 2020. Next-Generation Personal Authentication Scheme Based on EEG Signal and Deep Learning. Journal of Information Processing Systems, 16, 5, (2020), 1034-1047. DOI: 10.3745/JIPS.03.0147.