EEG Based Biometric Framework for Automatic Identity Verification

  • Ramaswamy Palaniappan
  • Danilo P. Mandic


The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud.


biometric Davies–Bouldin index electroencephalogram identity identification neural network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Pankanti, R.M. Bolle, and A. Jain, “Biometrics, the Future of Identification,” IEEE Computer (Special Issue on Biometrics), vol. 33, no. 2, 2000, pp. 46–49.Google Scholar
  2. 2.
    A.K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, 2004, pp. 4–20.CrossRefGoogle Scholar
  3. 3.
    D. Hurley, M. Nixon, and J. Carter, “Force Field Feature Extraction for Ear Biometrics,” Comput. Vis. Image Underst., vol. 98, no. 3, 2005, pp. 491–512.CrossRefGoogle Scholar
  4. 4.
    L. Biel, O. Pettersson, L. Philipson, and P. Wide, “ECG Analysis: A New Approach in Human Identification,” IEEE Trans. Instrum. Meas., vol. 50, no. 3, 2001, pp. 808–812.CrossRefGoogle Scholar
  5. 5.
    R. Palaniappan, “Method of identifying individuals using VEP signals and neural network,” IEE. Proc.- Sci. Meas. Technol., vol. 151, no. 1, 2004, pp.16–20.CrossRefGoogle Scholar
  6. 6.
    R.B. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles, “The Electroencephalogram as a Biometric,” Proc. CCECE, vol. 2, 2001 pp. 1363–1366.Google Scholar
  7. 7.
    M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou, “Person identification based on parametric processing of the EEG,” Proc. IEEE ICECS, vol. 1, 1999, pp. 283–286.Google Scholar
  8. 8.
    J. Polich, “P300 in Clinical applications: Meaning, method, and measurement,” in Electroencephalography Basic Principles, Clinical Applications, and Related Fields, E. Niedermeyer and F.L. da Silva (Eds.), William and Wilkins, Baltimore, 1993, pp. 1005–1018.Google Scholar
  9. 9.
    K.E. Misulis, “Spehlmann’s Evoked Potential Primer: Visual, Auditory and Somatosensory Evoked Potentials in Clinical Diagnosis,” Butterworth-Heinemann, UK, 1994.Google Scholar
  10. 10.
    J.L. Elman, “Finding structure in time,” Cogn. Sci., vol. 14, 1990, pp. 179–211.CrossRefGoogle Scholar
  11. 11.
    M. Riedmiller and H. Braun, “A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm,” Proc. IEEE ICNN, vol. 1, 1993, pp. 586–591.Google Scholar
  12. 12.
    R. Palaniappan, P. Raveendran, and S. Omatu, “EEG optimal channel selection using genetic algorithm for neural network classification of alcoholics,” IEEE Trans. Neural Netw., vol. 13, no. 2, 2002, pp. 486–491.CrossRefGoogle Scholar
  13. 13.
    E. Basar, “Memory and Brain Dynamics: Oscillations Integrating Attention, Perception, Learning, and Memory,” CRC, Boca Raton, 2004.CrossRefGoogle Scholar
  14. 14.
    J.G. Snodgrass, and M. Vanderwart, “A Standardized Set of 260 Pictures: Norms for Name Agreement, Image Agreement, Familiarity, and Visual Complexity,” J. Exp. Psychol. (Hum. Learn), vol. 6, no. 2, 1980, pp. 174–215.CrossRefGoogle Scholar
  15. 15.
    X.L. Zhang, H. Begleiter, B. Porjesz, W. Wang, and A. Litke, “Event Related Potentials During Object Recognition Tasks,” Brain Res. Bull., vol. 38, no. 6, 1995, pp. 531–538.CrossRefGoogle Scholar
  16. 16.
    A.M. Halliday (Ed.), “Evoked Potentials in Clinical Testing,” Churchill Livingstone, New York, 1993.Google Scholar
  17. 17.
    D.L. Davies and D.W. Bouldin, “A Cluster Separation Measure,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 1, no. 4, 1979, pp. 224–227.CrossRefGoogle Scholar
  18. 18.
    D.P. Mandic and J.A. Chambers, “Recurrent Neural Networks for Prediction,” Wiley, New York, 2001.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of EssexColchesterUK
  2. 2.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

Personalised recommendations