Abstract
Biometric recognition based on individual difference was commonly used in many aspects in life. Compared with the traditional features used in person identification, EEG-based biometry is an emerging research topic with high security and uniqueness, and it may open new research applications in the future. However, little work has been done within this area. In this paper, four feature extraction techniques were employed to characterize the resting EEG signals: AR model, time-domain power spectrum, frequency-domain power spectrum and phase locking value. In our experiments using 20 healthy subjects, the classification accuracy by support vector machine reached 90.52% with AR model parameters, highest of the four kinds of features. The results show the potential applications of resting EEG signal in person identification.
Chapter PDF
Similar content being viewed by others
References
Marcel, S., del R. Millán, J.: Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 743–748 (2007)
Clarke, R.: Human Identification in Information Systems: Management Challenges and Public Policy Issues. Information Technology & People 7, 6–37 (1994)
Marcel, S., del R. Millán, J.: Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 743–748 (2007)
Poulos, M., Rangoussi, M., Chrissikopoulos, V., Evangelou, A.: Person identification based on parametric processing of the EEG. In: The 6th IEEE International Conference on Electronics, Circuits and Systems, vol. 1, pp. 283–286 (1999)
Palaniappan, R., Ravi, K.V.R.: A new method to identify individuals using signals from the brain. In: The Joint Conference of the 4th International Conference on Information, Communications and Signal Processing, vol. 3, pp. 1442–1445 (2003)
Nakanishi, I., Baba, S., Miyamoto, C.: EEG based biometric authentication using new spectral features. In: International Symposium on Intelligent Signal Processing and Communication Systems, pp. 651–654 (2009)
Paranjape, R.B., Mahovsky, J., Benedicenti, L., Koles, Z.: The electroencephalogram as a biometric. In: Electrical and Computer Engineering (2001)
Poulos, M., Rangoussi, M., Alexandris, N., Evangelou, A.: Person identification from the EEG using nonlinear signal classification. Methods of Information in Medicine 41(1), 64–75 (2002)
Palaniappan, R.: Electroencephalogram signals from imagined activities: a novel biometric identifier for a small population. In: Belli, F., Radermacher, F.J. (eds.) IEA/AIE 1992. LNCS, vol. 604, pp. 604–611. Springer, Heidelberg (1992)
Lawhern, V., David Hairston, W., McDowell, K., Westerfield, M., Robbins, K.: Detection and classification of subject-generated artifacts in EEG signals using autoregressive models. Journal of Neuroscience Methods 208, 181–189 (2012)
Rosenblum, M.G., Pikovsky, A.S., Kurths, J.: Phase synchronization of chaotic oscillators. Phys. Rev. Lett. 76, 1804–1807 (1996)
Rosenblum, M., Pikovsky, A., Kurths, J., Schafer, C., Tass, P.A.: Phase synchronization: from theory to data analysis. In: Moss, F., Gielen, S. (eds.) Handbook of Biological Physics, vol. 4, pp. 279–321
Sazonov, A.V., Ho, C.K., Bergmans, J.W.M., Arends, J.B.A.M., Griep, P.A.M., Verbitskiy, E.A., Cluitmans, P.J.M., Boon, P.A.J.M.: An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG. Biological Cybernetics 100, 129–146 (2009)
Lachaux, J.-P., Rodriguez, E., Martinerie, J., Varela, F.J.: Measuring phase synchrony in brain signals. Human Brain Mapping 8, 194–208 (1999)
Jain, S., Deshpande, G.: Parametric modeling of brain signals. In: Proceedings of Technology for Life: North Carolina Symposium on Biotechnology and Bioinformatics, pp. 85–91 (2004)
Lal, T.N., Schröder, M., Hinterberger, T., Weston, J., Bogdan, M., Birbaumer, N., Schölkopf, B.: Support vector channel selection in BCI. IEEE Transations on Biomedical Engineering 51(6), 1003–1010 (2004)
Übeylï, E.D.: Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals. Expert System with Applications 37, 233–239 (2010)
Lawhern, V., David Hairston, W., McDowell, K., Westerfield, M., Robbins, K.: Detection and classification of subject-generated artifacts in EEG signals using autoregressive models. Journal of Neuroscience Methods 208, 181–189 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, R. et al. (2013). Feature Extraction of Individual Differences for Identification Recognition Based on Resting EEG. In: Rau, P.L.P. (eds) Cross-Cultural Design. Methods, Practice, and Case Studies. CCD 2013. Lecture Notes in Computer Science, vol 8023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39143-9_56
Download citation
DOI: https://doi.org/10.1007/978-3-642-39143-9_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39142-2
Online ISBN: 978-3-642-39143-9
eBook Packages: Computer ScienceComputer Science (R0)