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EEG Signals For Authentication In Security Systems

CSKH-02.2016 - (Tóm tắt) - Trong bài báo này, chúng tôi giới thiệu những phương pháp mới nhất và một số hướng nghiên cứu cho một hệ thống xác thực sử dụng sóng não.

Abstract— Authentication has been playing an important role in security systems and security operations. In a general sense, there are three types of person authentication: something a person knows (password-based), something a person has (token-based), and something a person is (biometric-based). Each has its own merits but also there are drawbacks which can cause vulnerabilities to security systems. Recently, technological advances make it easy to obtain Electroencephalography (EEG) signals. Moreover, the evidence shows that finding repeatable and stable brainwave patterns in EEG signals is feasible, and the prospect of using EEG signals for person authentication promising. An EEG-based person authentication system has the combined advantages of all three types of person authentication currently in use, yet without their drawbacks. Therefore, an EEG-based person authentication system should be suitable for especially high security systems. In this paper, we further speculate on that issue to provide a comprehensive review of state-of-the-art methods and some research directions for such an authentication system.

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 Tài liệu tham khảo

[1]. W. Stalling and L. Brown, “Computer security: Principles and practice-third edition”, William Stallings, 2015.

[2]. R. S. Sandhu and P. Samarati, “Access control: principle and practice”, Communications Magazine, IEEE, vol. 32, no. 9, pp. 40-48, 1994.

[3]. A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition”, Circuits and Systems for Video Technology, IEEE Transactions on, vol. 14, no. 1, pp. 4-20, 2004.

[4]. C. Rathgeb and A. Uhl, “A survey on biometric cryptosystems and cancelable biometrics”, EURASIP Journal on Information Security, vol. 2011, no. 1, pp. 1-25, 2011.

[5]. V. Matyas and Z. Riha, “Security of biometric authentication systems”, in Computer Information Systems and Industrial Management Applications (CISIM), 2010 International Conference on. IEEE, pp. 19-28, 2010.

[6]. S. Marcel and J. d. R. Millan, “Person authentication using brainwaves (eeg) and maximum a posteriori model adaptation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 4, pp. 743-752, 2007.

[7]. B. Obermaier, C. Neuper, C. Guger, and G. Pfurtscheller, “Information transfer rate in a five-classes brain-computer interface”, Neural Systems and Rehabilitation Engineering, IEEE Transactions, vol. 9, no. 3, pp. 283-288, 2001.

[8]. S. Sanei and J. A. Chambers, “EEG signal processing”, Wiley Interscience, 2008.

[9]. G. N. G. Molina, T. Ebrahimi, and J.-M. Vesin, “Joint time-frequency-space classification of eeg in a brain-computer in-terface application”, EURASIP journal on applied signal processing, vol. 2003, pp. 713-729, 2003.

[10]. F. Lotte, “Study of electroencephalographic signal processing and classification techniques towards the use of brain-computer interfaces in virtual reality applications”, Ph.D. dissertation, INSA de Rennes, 2008.

[11]. F. Sharbrough, G. Chatrian, R. Lesser, H. L¨ uders, M. Nuwer, and T. Picton, “American electroencephalographic society guidelines for standard electrode position nomenclature”, J. clin. Neurophysiol, vol. 8, no. 2, pp. 200-202, 1991.

[12]. E. A. Curran and M. J. Stokes, “Learning to control brain activity: a review of the production and control of eeg components for driving brain computer interface (BCI) systems”, Brain and cognition, vol. 51, no. 3, pp. 326-336, 2003.

[13]. P. Campisi and D. La Rocca, “Brain waves for automatic biometric based user recognition”, 2014.

[14]. M. Poulos, M. Rangoussi, N. Alexandris, A. Evangelou et al., “Person identification from the EEG using nonlinear signal classification”, Methods of information in Medicine, vol. 41, no. 1, pp. 64-75, 2002.

[15]. J. Berkhout and D. O. Walter, “Temporal stability and individual differences in the human EEG: an analysis of variance of spectral values”, IEEE Transactions on Biomedical Engineering, vol. 3, no. BME-15, pp. 165-168, 1968.

[16]. F. Vogel, “The genetic basis of the normal human electroen- cephalogram (EEG)”, Humangenetik, vol. 10, no. 2, pp. 91-114, 1970.

[17]. J. J. Lynch, D. A. Paskewitz, and M. T. Orne, “Inter-session stability of human alpha rhythm densities”, Electroencephalography and clinical neurophysiology, vol. 36, pp. 538-540, 1974.

[18]. B. P. Zietsch, J. L. Hansen, N. K. Hansell, G. M. Geffen, N. G. Martin, and M. J. Wright, “Common and specific genetic influences on EEG power bands delta, theta, alpha, and beta”, Biological psychology, vol. 75, no. 2, pp. 154-164, 2007.

[19]. T. Gasser, P. B¨ acher, and H. Steinberg, “Test-retest reliability of spectral parameters of the EEG”, Electroencephalography and clinical neurophysiology, vol. 60, no. 4, pp. 312-319, 1985.

[20]. M. Salinsky, B. Oken, and L. Morehead, “Test-retest reliability in EEG frequency analysis”, Electroencephalography and clinical neurophysiology, vol. 79, no. 5, pp. 382-392, 1991.

[21]. M. N¨ apflin, M. Wildi, and J. Sarnthein, “Test–retest reliability of resting eeg spectra validates a statistical signature of persons”, Clinical Neurophysiology, vol. 118, no. 11, pp. 2519-2524, 2007.

[22]. L. McEvoy, M. Smith, and A. Gevins, “Test–retest reliability of cognitive eeg”, Clinical Neurophysiology, vol. 111, no. 3, pp. 457-463, 2000.

[23]. D. La Rocca, P. Campisi, and G. Scarano, “On the repeatability of EEG features in a biometric recognition framework using a resting state protocol”, in BIOSIGNALS, pp. 419-428, 2013.

[24]. H. J. Lee, H. S. Kim, and K. S. Park, “A study on the reproducibility of biometric authentication based on electroen-cephalogram (EEG)”, in Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on. IEEE, pp.13-16, 2013.

[25]. R. Palaniappan and K. Revett, “Pin generation using EEG: a stability study”, International Journal of Biometrics, vol. 6, no. 2, pp. 95-105, 2014.

[26]. K.-Y. Lee and D. Jang, “Ethical and social issues behind brain computer interface”, in Brain-Computer Interface (BCI), 2013 International Winter Workshop on. IEEE, pp. 72-75, 2013,.

[27]. A. Stopczynski, D. Greenwood, L. K. Hansen, and A. Pentland, “Privacy for personal neuroinformatics”, Available at SSRN 2427564, 2014.

[28]. T. Bonaci, R. Calo, and H. J. Chizeck, “App stores for the brain: Privacy & security in brain-computer interfaces”, in Ethics in Science, Technology and Engineering, 2014 IEEE International Symposium on. IEEE, pp. 1-7, 2014.

[29]. C.-F. Lin, S.-H. Shih, and J.-D. Zhu, “Chaos based encryption system for encrypting electroencephalogram signals”, Journal of medical systems, vol. 38, no. 5, pp. 1-10, 2014.

[30]. P. T. Nguyen, “On EEG-based person recognition and human characteristics classification”, 2015.

[31]. H. Stassen, “Computerized recognition of persons by EEG spectral patterns”, Electroencephalography and clinical neurophysiology, vol. 49, no. 1, pp. 190-194, 1980.

[32]. J. Thorpe, P. C. van Oorschot, and A. Somayaji, “Pass thoughts: authenticating with our minds”, in Proceedings of the 2005 workshop on New security paradigms. ACM, 2005, pp. 45-56, 2005.

[33]. A. Martin, G. Doddington, T. Kamm, M. Ordowski, and M. Przybocki, “The det curve in assessment of detection task performance”, DTIC Document, Tech. Rep, 1997.

[34]. M. Fatourechi, A. Bashashati, R. K. Ward, and G. E. Birch, “Emg and eog artifacts in brain computer interface systems: A survey”, Clinical neurophysiology, vol. 118, no. 3, pp. 480-494, 2007.

[35]. A. Vallabhaneni, T. Wang, and B. He, “Braincomputer interface”, in Neural engineering. Springer, pp. 85-121, 2005.

[36]. R. Palaniappan, “Method of identifying individuals using vep signals and neural network”, IEE Proceedings-Science, Measurement and Technology, vol. 151, no. 1, pp. 16-20, 2004.

[37]. J. G. Snodgrass and M. Vanderwart, “A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity”, Journal of experimental psychology: Human learning and memory, vol. 6, no. 2, pp.174, 1980.

[38]. R. Palaniappan and D. P. Mandic, “Energy of brain potentials evoked during visual stimulus: A new biometric?”, in Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. Springer, pp. 735-740, 2005.

[39]. K. Ravi and R. Palaniappan, “Leave-one-out authentication of persons using 40 hz eeg oscillations”, in Computer as a Tool, 2005. EUROCON 2005. The International Conference on, vol. 2. IEEE, pp. 1386-1389, 2005.

[40]. R. Palaniappan, “Two-stage biometric authentication method using thought activity brain waves”, International Journal of Neural Systems, vol. 18, no. 01, pp. 59-66, 2008.

[41]. A. Z´ uquete, B. Quintela, and J. P. da Silva Cunha, “Biometric authentication using brain responses to visual stimulus”, In BIOSIGNALS, pp. 103-112, 2010.

[42]. S.-K. Yeom, H.-I. Suk, and S.-W. Lee, “Eeg-based person authentication using face stimulus”, in Brain-Computer Interface (BCI), 2013 International Winter Workshop on. IEEE, pp. 58-61, 2013.

[43]. Q. Gui, Z. Jin, and W. Xu, “Exploring eeg-based biometrics for user identification and authentication”, in Signal Processing in Medicine and Biology Symposium (SPMB), 2014 IEEE. IEEE, pp. 1-6, 2014.

[44]. C. He and Z. J. Wang, “An independent component analysis (ICA) based approach for eeg person authentication”, in Bioin-formatics and Biomedical Engineering, 2009. ICBBE 2009. 3rd International Conference on. IEEE, pp. 1-4, 2009.

[45]. C. He, X. Lv, and J. Wang, “Hashing the mar coefficients from EEG data for person authentication”, in Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. IEEE, pp. 1445-1448, 2009.

[46]. H. Jian-feng, “Biometric system based on eeg signals by feature combination”, in Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on, vol. 1. IEEE, pp. 752-755, 2010.

[47]. H. A. Shedeed, “A new method for person identification in a biometric security system based on brain EEG signal processing”, in Information and Communication Technologies (WICT), 2011 World Congress on. IEEE, pp. 1205-1210, 2011.

[48]. K. Ravi and R. Palaniappan, “Neural network classification of late gamma band electroencephalogram features”, Soft Computing, vol. 10, no. 2, pp. 163-169, 2006.

[49]. S. Mason, A. Bashashati, M. Fatourechi, K. Navarro, and G. Birch, “A comprehensive survey of brain interface technology designs”, Annals of biomedical engineering, vol. 35, no. 2, pp. 137-169, 2007.

[50]. F. Lotte, M. Congedo, A. L´ Ecuyer, F. Lamarche, B. Arnaldi et al., “A review of classification algorithms for EEG-based brain-computer interfaces”, Journal of neural engineering, vol. 4, 2007.

[51]. J. Makhoul, “Linear prediction: A tutorial review”, Proceedings of the IEEE, vol. 63, no. 4, pp. 561-580, 1975.

[52]. J. Pardey, S. Roberts, and L. Tarassenko, “A review of parametric modelling techniques for EEG analysis”, Medical engineering & physics, vol. 18, no. 1, pp. 2-11, 1996.

[53]. P. Stoica and R. L. Moses, “Spectral analysis of signals”, Pearson/Prentice Hall Upper Saddle River, NJ, 2005.

[54]. M. S. Bartlett, “Smoothing periodograms from time series with continuous spectra”, Nature, vol. 161, no. 4096, pp. 686-687, 1948.

[55]. P. Welch, “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, pp. 70-73, 1967.

[56]. M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou, “Person identification based on parametric processing of the EEG”, in Electronics, Circuits and Systems, 1999. Proceedings of ICECS’99. The 6th IEEE International Conference on, vol. 1. IEEE, pp. 283-286, 1999.

[57]. R. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles, “The electroencephalogram as a biometric”, in Electrical and Computer Engineering, 2001. Canadian Conference on, vol. 2. IEEE, pp. 1363-1366, 2001.

[58]. M. K. Abdullah, K. S. Subari, J. L. C. Loong, and N. N. Ah-mad, “Analysis of effective channel placement for an EEG-based biometric system”, in Biomedical Engineering and Sciences (IECBES), 2010 IEEE EMBS Conference on. IEEE, pp. 303-306, 2010.

[59]. K. Brigham and B. V. Kumar, “Subject identification from electroencephalogram (EEG) signals during imagined speech”, in Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on. IEEE, pp.1-8, 2010.

[60]. A. Riera, A. Soria-Frisch, M. Caparrini, C. Grau, and G. Ruffini, “Unobtrusive biometric system based on electroencephalogram analysis”, EURASIP Journal on Advances in Signal Processing, vol. 2008, pp. 18, 2008.

[61]. C. Ashby, A. Bhatia, F. Tenore, and J. Vogelstein, “Low-cost electroencephalogram (EEG) based authentication”, in Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on. IEEE, pp. 442-445, 2011.

[62]. P. Nguyen, D. Tran, X. Huang, and W. Ma, “Motor imagery EEG-based person verification”, in Advances in Computational Intelligence. Springer, pp. 430-438, 2013.

[63]. P. Nguyen, D. Tran, T. Le, X. Huang, and W. Ma, “EEG-based person verification using multi-sphere SVDD and UBM”, in Advances in Knowledge Discovery and Data Mining. Springer, pp. 289-300, 2013.

[64]. A. Flexer, “Data mining and electroencephalography”, Statistical Methods in Medical Research, vol. 9, no. 4, pp. 395-413, 2000.

[65]. X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, S. Y. Philip et al, “Top 10 algorithms in data mining”, Knowledge and Information Systems, vol. 14, no. 1, pp. 1-37, 2008.

[66]. B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers”, in Proceedings of the feifth annual workshop on Computational learning theory. ACM, pp. 144-152, 1992.

[67]. C. Cortes and V. Vapnik, “Support-vector networks”, Machine learning, vol. 20, no. 3, pp. 273-297, 1995.

[68]. D. M. Tax and R. P. Duin, “Support vector data description”, Machine learning, vol. 54, no. 1, pp. 45-66, 2004.

[69]. K. Das, S. Zhang, B. Giesbrecht, and M. P. Eckstein, “Using rapid visually evoked EEG activity for person identification”, in Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. IEEE, pp. 2490-2493, 2009.

[70]. C.-C. Chang and C.-J. Lin, “Libsvm: A library for support vector machines”, ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, pp. 27, 2011.

[71]. Z. Dan, Z. Xifeng, and G. Qiangang, “An identification system based on portable EEG acquisition equipment”, in Intelligent System Design and Engineering Applications (ISDEA), 2013 Third International Conference on. IEEE, pp. 281-284, 2013.

[72]. S. Sun, “Multitask learning for EEG-based biometrics,” in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. IEEE, pp. 1-4, 2008.

[73]. J. Chuang, H. Nguyen, C. Wang, and B. Johnson, “I think, therefore I am: Usability and security of authentication using brainwaves,” in Financial Cryptography and Data Security. Springer, 2013, pp. 1-16, 2013.

[74]. http://fingerchip.pagespersoorange.fr/biometrics/ accuracy.html.

Phạm Tiến Dũng, Đinh Hoàng Gia, Lê Khải, Đào Thị Hồng Vân