DocumentCode :
1797679
Title :
Multi-factor EEG-based user authentication
Author :
Tien Pham ; Wanli Ma ; Dat Tran ; Phuoc Nguyen ; Dinh Phung
Author_Institution :
Fac. of Educ., Univ. of Canberra, Canberra, ACT, Australia
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
4029
Lastpage :
4034
Abstract :
Electroencephalography (EEG) signal has been used widely in health and medical fields. It is also used in brain-computer interface (BCI) systems for humans to continuously control mobile robots and wheelchairs. Recently, the research communities successfully explore the potential of using EEG as a new type of biometrics in user authentication. EEG-based user authentication systems have the combined advantages of both password-based and biometric-based authentication systems, yet without their drawbacks. In this paper, we propose to take the advantage of rich information, such as age and gender, carried by EEG signals for user authentication in multi-level security systems. Our experiments showed very promising results for the proposed multi-factor EEG-based authentication method.
Keywords :
biometrics (access control); brain-computer interfaces; electroencephalography; medical signal processing; BCI systems; EEG based user authentication systems; EEG signal; biometrics; brain computer interface; continuously control mobile robots; electroencephalography signal; health fields; medical fields; multifactor EEG; multilevel security systems; user authentication; wheelchairs; Authentication; Brain models; Electroencephalography; Feature extraction; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889569
Filename :
6889569
Link To Document :
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