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

21 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, IJCNN 2014 - 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, IJCNN 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

Fingerprint

Electroencephalography
Authentication
Biometrics
Brain computer interface
Wheelchairs
Bioelectric potentials
Security systems
Mobile robots
Health
Experiments

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). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2014.6889569
Pham, Tien ; MA, Wanli ; TRAN, Dat ; Nguyen, Phuoc ; PHUNG, Dinh. / Multi-factor EEG-based user authentication. 2014 International Joint Conference on Neural Networks (IJCNN). editor / Derong Liu ; Jennie Si. USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. pp. 4029-4034 (Proceedings of the International Joint Conference on Neural Networks).
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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.",
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author = "Tien Pham and Wanli MA and Dat TRAN and Phuoc Nguyen and Dinh PHUNG",
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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). Proceedings of the International Joint Conference on Neural Networks, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 4029-4034, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889569

Multi-factor EEG-based user authentication. / Pham, Tien; MA, Wanli; TRAN, Dat; Nguyen, Phuoc; PHUNG, Dinh.

2014 International Joint Conference on Neural Networks (IJCNN). ed. / Derong Liu; Jennie Si. USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 4029-4034 (Proceedings of the International Joint Conference on Neural Networks).

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

TY - GEN

T1 - Multi-factor EEG-based user authentication

AU - Pham, Tien

AU - MA, Wanli

AU - TRAN, Dat

AU - Nguyen, Phuoc

AU - PHUNG, Dinh

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N2 - 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.

AB - 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.

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T3 - Proceedings of the International Joint Conference on Neural Networks

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