Abstract
This paper investigates a method to generate personal identification number (PIN) using brain activity recorded from a single active electroencephalogram (EEG) channel. EEG based biometric to generate PIN is less prone to fraud and the method is based on the recent developments in brain-computer interface (BCI) technology, specifically P300 based BCI designs. Our perfect classification accuracies from three subjects indicate promise for generating PIN using thought activity measured from a single channel.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wayman, J., Jain, A., Maltoni, D., Maio, D. (eds.): Biometric Systems: Technology, Design and Performance Evaluation. Springer, Heidelberg (2004)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clinical Neurophysiology 113(6), 767–791 (2002)
Palaniappan, R., Mandic, D.P.: Biometric from the brain electrical activity: A machine learning approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 738–742 (2007)
Ravi, K.V.R., Palaniapan, R.: Improving visual evoked potential feature classification for person recognition using PCA and normalization. Pattern Recognition Letters 27(7), 726–733 (2006)
Neurosky, http://www.neurosky.com
Cho, H.-s., Goo, J.J., Suh, D., Park, K.S., Hahn, M.: The virtual reality brain-computer interface system for ubiquitous home control. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 992–996. Springer, Heidelberg (2006)
Geng, T., Gan, J.Q., Hu, H.: A self-paced online BCI for mobile robot control. International Journal of Advanced Mechatronic Systems 2(1-2), 28–35 (2010)
Krusienski, D.J., Sellers, E.W., Cabestaing, F., Bayoudh, S., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: A comparison of classification techniques for the P300 speller. Journal of Neural Engineering 3, 299–305 (2006)
Gupta, C.N., Palaniappan, R.: Enhanced detection of visual evoked potentials in brain-computer interface using genetic algorithm and cyclostationary analysis. Computational Intelligence and Neuroscience (Special Issue on Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications)Â 2007, Article ID 28692, 12 pages (2007), doi:10.1155/2007/28692
Hoffmann, U., Vesin, J.M., Ebrahimi, T., Diserens, K.: An efficient P300-based brain-computer interface for disabled subjects. Journal of Neuroscience Methods 167(1), 115–125 (2007)
Obermaier, B., Neuper, C., Guger, C., Pfurtscheller, G.: Information transfer rate in a five-classes brain–computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 9(3), 283–288 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Palaniappan, R., Gosalia, J., Revett, K., Samraj, A. (2011). PIN Generation Using Single Channel EEG Biometric. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22726-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-22726-4_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22725-7
Online ISBN: 978-3-642-22726-4
eBook Packages: Computer ScienceComputer Science (R0)