@inproceedings{6901c684e29740d6b35b8d63121c87bf,
title = "Multi-factor EEG-based user authentication",
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 = "EEG-user-authentication, brain-computer-interfaces",
author = "Tien Pham and Wanli MA and Dat TRAN and Phuoc Nguyen and Dinh PHUNG",
year = "2014",
doi = "10.1109/IJCNN.2014.6889569",
language = "English",
isbn = "9781479914821",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "4029--4034",
editor = "Derong Liu and Jennie Si",
booktitle = "2014 International Joint Conference on Neural Networks (IJCNN)",
address = "United States",
note = "2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",
}