Recent advances in signal processing have made possible the use of brain waves or EEG signals for user recognition and also for communication between human and computers. Electroencephalography (EEG) is sensitive to electrical field generated by the electric currents in the brain, and EEG recordings are acquired with portable and relatively inexpensive devices when compare to the other brain imaging techniques. EEG signals are representative signals containing the information about state of human brain. EEG signals are sometimes uses for clinical applications for medical diagnostics. The shape of the wave may contain useful information about the state of the brain. It has been known that different regions of the brain are activated according to the associated mental status, for example, emotional status, cognitive status, etc. Since the difference in activities of the brain causes the difference in characteristics of EEG, it has been attempted to investigate the brain activity through analyzing EEG.

Electroencephalography (EEG), brain rhythms, biometrics, Brain Computer Interfacing (BCI), Feature Extraction, Auto-regression, Classification

[1] Patrizio Campisi , Daria La Rocca, “Brain Waves for Automatic Biometric-Based User Recognition” IEEE Trans on Info Forensics and security , vol. 9, no. 5, may 2014.
[2] Zhenghui Gu, Zhuliang Yu, Zhifang Shen, and Yuanqing Li “An Online Semi-supervised Brain Computer Interface ” IEEE Tran On Biomedical Engg, VOL. 60, NO. 9, 2013
[3] J. Klonovs, C. Petersen, H. Olesen, and A. Hammershoj, “ID proof on the go: Development of a mobile EEG-based biometric authentication system,” IEEE Veh. Technol. Mag., vol. 8, no. 1, Mar. 2013.
[4] C. He and Z. J. Wang, “An independent component analysis (ICA) based approach for EEG person authentication,” in Proc. 3rd ICBBE, 2010.pp.1-10
[5] Ramaswamy Palaniappan, “Two-stage biometric authentication method using Thought activity brain waves”, Department of Computing and Electronic Systems, University of Essex, Colchester, CO4 3SQ, United Kingdom.
[6] Khalid, M.B.; Rao, N.I.; Rizwan-i-Haque, I.; Munir, S.; Tahir, F., “Towards a Brain Computer Interface Using Wavelet Transform with Averaged and Time Segmented Adapted Wavelets”, In Proceedings of the 2nd International Conference on Computer, Control and Communication (IC4’09) Karachi, Sindh, Pakistan, February 2009; pp. 1–4.
[7] M. Näpflin, 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.
[8] Y. Boytsova and S. Danko, “EEG differences between resting states with eyes open and closed in darkness,” Human Physiol., vol. 36, no. 3, pp. 367–369, 2010.
[9] R. Palaniappan, “Two-stage biometric authentication method using thought activity brain waves,” Int. J. Neural Syst., vol. 18, no. 1, pp. 59–66, 2008.
[10] C. He and Z. J. Wang, “An independent component analysis (ICA) based approach for EEG person authentication,” in Proc. 3rd ICBBE, 2010, pp. 1–10.
[11] T. O’Donoghue et al., “A NOS1 variant implicated in cognitive performance influences evoked neural responses during a high density EEG study of early visual perception,” Human Brain Mapping, vol. 33, no. 5, pp. 1202–1211, 2012.
[12] M. Fernandes et al., “Wearable brain cap with contactless electroencephalogram measurement for brain-computer interface applications,” in Proc. 4th Int. IEEE EMBS Conf. Neural Eng., Apr./May 2009, pp. 387–390.