EEG-Based User Authentication Using Artifacts

  • Tien Pham
  • Wanli Ma
  • Dat Tran
  • Phuoc Nguyen
  • Dinh Phung
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)


Recently, electroencephalography (EEG) is considered as a new potential type of user authentication with many security advantages of being difficult to fake, impossible to observe or intercept, unique, and alive person recording require. The difficulty is that EEG signals are very weak and subject to the contamination from many artifact signals. However, for the applications in human health, true EEG signals, without the contamination, is highly desirable, but for the purposes of authentication, where stable and repeatable patterns from the source signals are critical, the origins of the signals are of less concern. In this paper, we propose an EEG-based authentication method, which is simple to implement and easy to use, by taking the advantage of EEG artifacts, generated by a number of purposely designed voluntary facial muscle movements. These tasks can be single or combined, depending on the level of security required. Our experiment showed that using EEG artifacts for user authentication in multilevel security systems is promising.


EEG authentication security biometrics pattern recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brigham, K., Kumar, B.V.K.V.: Subject Identification from Electroencephalogram (EEG) Signals During Imagined Speech. In: Proc. IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems (BTAS 2010) (2010)Google Scholar
  2. 2.
    Brown, L.: Computer Security: Principles and Practice. William Stallings (2008)Google Scholar
  3. 3.
    Burges, J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  4. 4.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011)Google Scholar
  5. 5.
    Dix, A.: Human–computer interaction: A stable discipline, a nascent science, and the growth of the long tail. Interacting with Computers 22(1), 13–27 (2010)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Emotiv EPOC headset,
  7. 7.
    Experiment Wizard software tool,
  8. 8.
    Hall, M., et al.: The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  9. 9.
    Lenman, S., Bretzner, L., Thuresson, B.: Using marking menus to develop command sets for computer vision based hand gesture interfaces. In: Proceedings of the Second Nordic Conference on Human-Computer Interaction 2002, pp. 239–242. ACM, Aarhus (2002)Google Scholar
  10. 10.
    Marcel, S., Millán, J.R.: Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 743–752 (2007)CrossRefGoogle Scholar
  11. 11.
    Nakagawa, S., Wang, L., Ohtsuka, S.: Speaker Identification and Verification by Combining MFCC and Phase Information. IEEE Transactions on Audio, Speech, and Language Processing 20, 1085–1095 (2012)CrossRefGoogle Scholar
  12. 12.
    Nina, H., et al.: Integrating cognitive load theory and concepts of human–computer interaction. Computers in Human Behavior 26(6), 1278–1288 (2010)CrossRefGoogle Scholar
  13. 13.
    Nguyen, P., Tran, D., Huang, X., Ma, W.: Motor Imagery EEG-Based Person Verification. In: Rojas, I., Joya, G., Cabestany, J. (eds.) IWANN 2013, Part II. LNCS, vol. 7903, pp. 430–438. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Palaniappan, R.: Two-stage biometric authentication method using thought activity brain waves. International Journal of Neural Systems 18 (2008)Google Scholar
  15. 15.
    Safont, G., Salazar, A., Soriano, A., Vergara, L.: Combination of multiple detectors for EEG based biometric identification/authentication. In: 2012 IEEE International Carnahan Conference on Security Technology (ICCST), pp. 230–236 (2012)Google Scholar
  16. 16.
    Sanei, S., Chambers, J.: EEG signal processing. Wiley-Interscience (2007)Google Scholar
  17. 17.
    Sun, S.: Multitask learning for EEG-based biometrics. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (2008)Google Scholar
  18. 18.
    Turk, M., Robertson, G.: Perceptual user interfaces. Communications of the ACM 43(3) (2000)Google Scholar
  19. 19.
    Welch, P.: The use of Fast Fourier Transform for the estimation of power spectra: a method based on time averaging over short, modified periodogram. IEEE Trans. Audio Electroacoustics, 70–73 (1967)Google Scholar
  20. 20.
    Zhao, W., Zhang, H.: Secure Fingerprint Recognition Based on Frobenius Norm. In: 2012 International Conference on Computer Science and Electronics Engineering, vol. 1, pp. 388–391 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tien Pham
    • 1
  • Wanli Ma
    • 1
  • Dat Tran
    • 1
  • Phuoc Nguyen
    • 1
  • Dinh Phung
    • 1
  1. 1.Faculty of Education, Science, Technology and MathematicsUniversity of CanberraCanberraAustralia

Personalised recommendations