Systems, Man & Cybernetics - October 2017 - 14
effects of epilepsy on identification rate. Further investigation with more data sets studying unhealthy individuals is
necessary for confirming the influences of various brain disorders on personal identification. It is also worthwhile to
explore multimodal biometric authentication systems using
multiple features in EEG, face, and electrooculograms
(EOGs). The use of EOGs is a reliable choice for improving
EEGBS performance in a multimodal scheme, because measurement can be made using the same electrodes.
He has published more than 220 papers and serves as an
associate editor of IEEE Transactions on Human-Machine
Systems; associate editor of Circuits, Systems, and Signal
Processing; and technical committee cochair of Brain-
Machine Interface Systems of the IEEE Systems, Man, and
Cybernetics Society. He is a Senior Member of the IEEE.
References
[1] Y. Jucheng, N. Xiong, and A. V. Vasilakos, "Two-stage enhancement scheme for lowquality fingerprint images by learning from the images," IEEE Trans. Human-Mach.
Conclusion
Until now, no biometric feature has proven to be the best for
every human being, despite substantial advances in technology and science. The great potential of brain-wave-based
biometrics has been identified recently, and there is fastgrowing interest from the scientific community regarding
its practical deployment. The fact that the literature thoroughly validates the assumption that EEG signals contain
genetic information that remains stable across time has presented the opportunity to stimulate further research in
addressing the ongoing challenges faced in the field of EEG
biometrics and transfer EEGBS from laboratories to commercial applications. Future studies must focus on optimizing EEG recording protocols, channel locations, signal
processing, and machine-learning techniques to achieve
systems with increased uniqueness, collectability, acceptability, and robustness to circumvention. Efficient methodologies analyzing larger data sets including healthy as well
as unhealthy subjects must also be evaluated.
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Acknowledgment
This work was supported by the Academic Research Fund
RG145/14, Ministry of Education, Singapore.
[9] H. H. Stassen, "Computerized recognition of persons by EEG spectral patterns,"
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[10] M. Del Pozo-Banos, C. M. Travieso, C. T. Weidemann, and J. B. Alonso, "EEG biometric identification: A thorough exploration of the time-frequency domain," J. Neural
About the Authors
Kavitha P. Thomas (ptkavitha@ntu.edu.sg) earned her
B.Tech. degree (first honors) in electrical and electronics
engineering from Mahatma Gandhi University, Kerala,
India, in 2002 and her Ph.D. degree in computer engineering from Nanyang Technological University (NTU), Singapore, in 2011. Since 2011, she has been a postdoctoral
research fellow at the School of Computer Science and
Engineering, NTU, Singapore. Her major research interests
include digital signal processing, pattern recognition, and
brain-computer interface.
A.P. Vinod (asvinod@ntu.edu.sg) earned his B.Tech.
degree in instrumentation and control engineering from the
University of Calicut, India, in 1993 and his M.Eng. and Ph.D.
degrees from the School of Computer Engineering, Nanyang
Technological University (NTU), Singapore, in 2000 and
2004, respectively. From 2000 to 2002, he was a lecturer at
Singapore Polytechnic and joined NTU as a lecturer in the
School of Computer Engineering in September 2002, became
an assistant professor in 2004, and a tenured associate professor from 2010 to 2017; he will join the Indian Institute of
Technology, Palakkad, India, as professor in October 2017.
14
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Oc tob e r 2017
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concerns," IEEE Security Privacy, vol. 99, no. 2, pp. 33-42, 2003.
[12] D. S. Bassett and M. S. Gazzaniga, "Understanding complexity in the human
brain," Trends Cognitive Sci., vol. 15, no. 5, pp. 200-209, 2011.
[13] R. Palaniappan and D. P. Mandic, "Biometrics from brain electrical activity: A
machine learning approach," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4,
pp. 738-742, 2007.
[14] S. Marcel and J. D. R. Millan, "Person authentication using brainwaves (EEG) and
maximum a posteriori model adaptation," IEEE Trans. Pattern Anal. Mach. Intell.,
vol. 29, no. 4, pp. 743-748, 2006.
[15] T. Gasser, P. Bacher, and H. Steinberg, "Test-retest reliability of spectral parameters of the EEG," Electroencephalography Clin. Neurophysiol., vol. 60, no. 4, pp.
312-319, 1985.
[16] E. Niedermeyer and F. Lopes da Silva, Eds., Electroencephalography: Basic
Principles, Clinical Applications, and Related Fields. Philadelphia, PA: Lippincott
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[17] M. Napflin, M. Wildi, and J. Sarnthein, "Test-retest reliability of resting EEG
spectra validates a statistical signature of persons," Clin. Neurophysiol., vol. 118, no.
11, pp. 2519-2524, 2007.
[18] P. Campisi and D. La Rocca, "Brain waves for automatic biometric based user recognition," IEEE Tran. Inf. Forensics Security, vol. 9, no. 5, pp. 782-800, 2014.
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