Skip to main content
SpringerLink
Log in

EEG Based Biometric Framework for Automatic Identity Verification

  • Published:
The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. K.E. Misulis, “Spehlmann’s Evoked Potential Primer: Visual, Auditory and Somatosensory Evoked Potentials in Clinical Diagnosis,” Butterworth-Heinemann, UK, 1994.

  10. J.L. Elman, “Finding structure in time,” Cogn. Sci., vol. 14, 1990, pp. 179–211.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  13. E. Basar, “Memory and Brain Dynamics: Oscillations Integrating Attention, Perception, Learning, and Memory,” CRC, Boca Raton, 2004.

    Book  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  16. A.M. Halliday (Ed.), “Evoked Potentials in Clinical Testing,” Churchill Livingstone, New York, 1993.

  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.

    Article  Google Scholar 

  18. D.P. Mandic and J.A. Chambers, “Recurrent Neural Networks for Prediction,” Wiley, New York, 2001.

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Essex, Colchester, Essex, CO4 3SQ, UK

    Ramaswamy Palaniappan

  2. Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Danilo P. Mandic

Authors
  1. Ramaswamy Palaniappan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danilo P. Mandic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramaswamy Palaniappan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Palaniappan, R., Mandic, D.P. EEG Based Biometric Framework for Automatic Identity Verification. J VLSI Sign Process Syst Sign Im 49, 243–250 (2007). https://doi.org/10.1007/s11265-007-0078-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-007-0078-1

Keywords

Search

Navigation