Electrophysiological Biometrics: Opportunities and Risks

  • Alejandro Riera
  • Stephen Dunne
  • Iván Cester
  • Giulio Ruffini
Part of the The International Library of Ethics, Law and Technology book series (ELTE, volume 11)


The use of electrophysiological signals as features to authenticate subjects is a novel approach to biometrics. It has been proven that both electrocardiography (ECG) and electroencephalography (EEG) signals are unique enough to be applied for recognition and identification purposes. Moreover, the use of electrooculography (EOG) and electromyography (EMG), which are related to the movement of the eyes and muscular activity, can also be useful and add an extra dimension to the field of biometrics: the possibility of continuous and transparent biometrics, i.e., biometry on the move. We also comment on the future of the electrophysiological biometrics, highlighting the added value. This includes the use of a Brain Computer Interface (BCI) system for authentication purposes and the application of such a system for the evolving field of telepresence and virtual reality.


Equal Error Rate Brain Computer Interface Biometric System Imagery Task Iris Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





Brain computer interface


Cross correlation








Equal error rate






Event related potential


European Union


Framework program


False positive rate


Fourier transform




Mutual information


True positive rate


Universal serial bus



The authors wish to acknowledge the ACTIBIO project, a STREP collaborative project supported under the 7th Framework Program (Grant agreement number: FP7-ICT-2007-1-215372) in which Starlab is actively involved. ACTIBIO aims at authenticating subjects in a transparent way by monitoring their activities by means of novel biometric modalities.


  1. Andreassi, J.L. 2007. Psychophysiology: Human behavior and physiological response, 5th ed. Mahwah/London: Lawrence Erlbaum Associates Publishers.Google Scholar
  2. Arjunan, S.P., and D.K. Kumar. 2007. Fractal based modelling and analysis of electromyography (EMG) to identify subtle actions. Conference Proceeding of IEEE Engineering in Medicine and Biology Society 2007: 1961–1964.Google Scholar
  3. Biel, L., et al. 2001. ECG analysis: A new approach in human identification. IEEE Transactions on Instrumentation and Measurement 50: 808–812.CrossRefGoogle Scholar
  4. Chang, C. 2005. Human identification using one lead ECG. Master thesis. Dep Comput Sci Inf Eng. Chaoyang Univ Technol (Taiwan).Google Scholar
  5. Cohn, J., et al. 2002. Individual differences in facial expression: Stability over time, relation to self-reported emotion, and ability to inform person identification. In Proceedings of the International Conference on Multimodal User Interfaces. Washington, DC: IEEE Computer Society.Google Scholar
  6. Costa, T., et al. 2006. EEG phase synchronization during emotional response to positive and ­negative film stimuli. Neuroscience Letters. doi: 10.1016/j.neulet.2006.06.039.
  7. Galvani, L. 1791. De viribus electricitatis in motu musculari: Commentarius. Bologna: Tip. Istituto delle Scienze, p 58.4 tavv. f.  t.; in 4.; DCC.f.70.Google Scholar
  8. Goldstein, I.B. 1972. Electromyography: A measure of skeletal muscle response. In Handbook of psychophysiology, ed. N.S. Greenfield and R.A. Sternbach, 329–365. New York: Holt, Rinehart & Winston.Google Scholar
  9. Graff, C., et al. 2007. Physiological signals as potential measures of individual biometric characteristics and recommendations for system development. Deliv D2.1, EU IST HUMABIO Project (IST-2004-026990).Google Scholar
  10. Guger, C., et al. 2009. Brain-computer interface for virtual reality control. Proc ESANN: 443–448.Google Scholar
  11. Higuchi, T. 1988. Approach to irregular time series on the basis of the fractal theory. Pfysica D 31: 277–283.CrossRefGoogle Scholar
  12. Israel, S., et al. 2005. ECG to identify individuals. Pattern Recognition. doi: 10.1016/j.patcog.2004.05.014.
  13. Kandel, E. 1981. Principles of neural science. New York: Elsevier.Google Scholar
  14. Kleissen, R. 1998. Electromyography in the biomechanical analysis of human movement and its clinical application. Gait & Posture 8: 143–158.CrossRefGoogle Scholar
  15. Kyoso, M. 2001. Development of an ECG identification system. In Proceedings of 23rd Annual International IEEE Conference of Engineering in Medicine and Biology Society, 4: 3721–3723.Google Scholar
  16. Ljubomir, A., et al. 1996. Non-linear analysis of emotion EEG: Calculation of Kolmogorov entropy and the principal Lyapunov exponent. Neuroscience Letters. doi: 10.1016/S0304-3940(97)
  17. Llobera, J. 2007. Narratives within Immersive Technologies. arXiv:0704.2542.Google Scholar
  18. Marcel, S., and J. Millán. 2007. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/TPAMI.2007.1012.
  19. Mohammadi, G., et al. 2006. Person identification by using AR model for EEG signals. In Proceedings of 9th International Conference on Bioengineering Technology. Czech Republic.Google Scholar
  20. Moukabary, T. 2007. Willem Einthoven (1860–1927): Father of electrocardiography. Cardiology Journal 14: 316–317.Google Scholar
  21. Neher, E., and B. Sakmann. 1992. The patch clamp technique. Scientific American 266(3): 44–51.CrossRefGoogle Scholar
  22. Palaniappan, R., and S.M. Krishnan. 2004. Identifying individuals using ECG beats. In Proceedings of the International Conference Signal Processing Communication, 569–572. Bangalore. ISBN: 0-7803-8674-4.Google Scholar
  23. Paranjape, R., et al. 2001. The electroencephalogram as a biometric. Proceeding of the Canadian Conference on Electrical and Computer Engineering. doi: 10.1109/CCECE.2001.933649.
  24. Poulos, M., et al. 1998. Person identification via the EEG using computational geometry ­algorithms. In Proceedings of the Ninth European Signal Processing, 2125–2128. Rhodes. ISBN 960-7620-05-4.Google Scholar
  25. Poulos, M., et al. 1999. Parametric person identification from EEG using computational geometry. In Proceedings of the 6th International Conference on Electron, Circuits and System (ICECS’99). doi:  10.1109/ICECS.1999.813403.
  26. Poulos, M., et al. 2001. On the use of EEG features towards person identification via neural ­networks. Médical Informatics & the Internet in Medicine. doi: 10.1080/14639230118937.
  27. Poulos, M., et al. 2002. Person identification from the EEG using nonlinear signal classification. Methods of Information in Medicine 41: 64–75.Google Scholar
  28. Rainville, P., et al. 2006. Basic emotions are associated with distinct patterns of cardiorespiratory activity. International Journal of Psychophysiology. doi: 10.1016/j.ijpsycho.2005.10.024.
  29. Riera, A., et al. 2008a. Unobtrusive biometric system based on electroencephalogram analysis. Hindawi Journal of Advances in Signal Processing. doi: 10.1155/2008/143728.
  30. Riera, A., et al. 2008b. STARFAST: A wireless wearable EEG/ECG biometric system based on the ENOBIO sensor. Phealth Proceedings of the 5th International Workshop on Wearable Micro and Nanosystems for Personalised Health.Google Scholar
  31. Ruffini, G., et al. 2006. A dry electrophysiology electrode using CNT arrays. Sensors and Actuators. doi: 10.1016/j.sna.2006.06.013.
  32. Ruffini, G., et al. 2007. ENOBIO dry electrophysiology electrode; first human trial plus wireless electrode system. In Proceedings of the 29th IEEE EMBS Annual International Conference. Lyon.  10.1109/IEMBS.2007.4353895.
  33. Song, Y., et al. 2009. Active microelectronic neurosensor arrays for implantable brain communication interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering 17(4): 339–345.CrossRefGoogle Scholar
  34. Swartz, B.E. 1998. Timeline of the history of EEG and associated fields. Electroencephalography and Clinical Neurophysiology 106: 173–176.CrossRefGoogle Scholar
  35. Vianna, E., and D. Tranel. 2006. Gastric myoelectrical activity as an index of emotion arousal. Psychophysiology. doi: 10.1016/j.ijpsycho.2005.10.019.
  36. Waller, A.D. 1887. A demonstration on man of electromotive changes accompanying the hearts beat. The Journal of Physiology 8: 229–234.Google Scholar
  37. Wang, Y., et al. 2006. Phase synchrony measurement in motor cortex for classifying. Single-trial EEG during motor imagery. In Proceedings of the 28th IEEE EMBS Annual International Conference. New York. doi: 10.1109/IEMBS.2006.259673.
  38. Welch, P.D. 1967. The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics AU-15(2): 70–73.CrossRefGoogle Scholar
  39. Winges, S., and M. Santello. 2005. From single motor unit activity to multiple grip forces: Mini-review of multi-digit grasping. Integrative and Comparative Biology. doi: 10.1093/icb/45.4.679.
  40. Zhang, D.D. 2000. Automated biometrics: Technologies and systems. Heidelberg: Springer.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Alejandro Riera
    • 1
  • Stephen Dunne
    • 1
  • Iván Cester
    • 1
  • Giulio Ruffini
    • 1
  1. 1.Starlab Barcelona S.L.BarcelonaSpain

Personalised recommendations