Multi-factor EEG-based user authentication

Tien Pham, Wanli MA, Dat TRAN, Phuoc Nguyen, Dinh PHUNG

Research output: A Conference proceeding or a Chapter in BookConference contribution

26 Citations (Scopus)
1 Downloads (Pure)

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.
Original languageEnglish
Title of host publication2014 International Joint Conference on Neural Networks (IJCNN)
EditorsDerong Liu, Jennie Si
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4029-4034
Number of pages6
ISBN (Electronic)9781479914845, 9781479966271
ISBN (Print)9781479914821
DOIs
Publication statusPublished - 2014
Event2014 International Joint Conference on Neural Networks - Beijing, Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

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  • Cite this

    Pham, T., MA, W., TRAN, D., Nguyen, P., & PHUNG, D. (2014). Multi-factor EEG-based user authentication. In D. Liu, & J. Si (Eds.), 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 4029-4034). (Proceedings of the International Joint Conference on Neural Networks). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2014.6889569