• 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