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

    20 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

    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). 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
    @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",
    pages = "4029--4034",
    editor = "Derong Liu and Jennie Si",
    booktitle = "2014 International Joint Conference on Neural Networks (IJCNN)",
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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). 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.

    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

    PY - 2014

    Y1 - 2014

    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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    KW - brain-computer-interfaces

    UR - http://ieeexplore.ieee.org/document/6889569/?arnumber=6889569

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    DO - 10.1109/IJCNN.2014.6889569

    M3 - Conference contribution

    SN - 9781479914821

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    BT - 2014 International Joint Conference on Neural Networks (IJCNN)

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    A2 - Si, Jennie

    PB - IEEE, Institute of Electrical and Electronics Engineers

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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 https://doi.org/10.1109/IJCNN.2014.6889569