Abstract
User authentication plays an important role in security systems. In general, there are three types of authentications: password based, token based, and biometrics based. Each of them has its own merits and drawbacks. Recently, the research communities successfully explore the possibility that electroencephalography (EEG) being as a new type of biometrics in person recognition, and hence the prospect of using EEG in user authentication is promising. An EEG-based user authentication system has the combined advantages of both password based and biometric based authentication systems, yet without their drawbacks. In this paper we propose to use EEG to authenticate users in multilevel security systems where users are asked to provide EEG signal for authentication by performing motor imagery tasks. These tasks can be single or combined, depending on the level of security required. The analysis and processing of EEG signals of motor imagery will be presented through our experimental results.
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References
Allison, B.: Trends in BCI research: progress today, backlash tomorrow? The ACM Magazine for Students 18, 18–22 (2011)
Ashby, C., Bhatia, A., Tenore, F., Vogelstein, J.: Low-cost electroencephalogram (EEG) based authentication. In: 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 442–445 (2011)
Brown, L.: Computer Security: Principles and Practice. William Stallings (2008)
Burges, J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Flexer, A.: Data mining and electroencephalography. Statistical Method in Medical Research 9, 395–413 (2000)
He, C., Chen, H., Wang, Z.: Hashing the Coefficients from EEG Data for Person Authentication. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, pp. 1445–1448 (2009)
Hu, J.: Biometric System based on EEG Signals by feature combination. In: 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 752–755 (2010)
Leeb, R., Brunner, C., Muller-Putz, G., Schlogl, A., Pfurtscheller, G.: BCI Competition 2008 - Graz data set B (2008), http://www.bbci.de/competition/iv/
Marcel, S., Millán, J.R.: Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 743–752 (2007)
Ma, W., Campell, J., Tran, D., Kleeman, D.: Password Entropy and Password Quality. In: 2010 4th International Conference on 2010 Network and System Security (NSS), 583–587 (2010)
Nguyen, P., Tran, D., Le, T., Hoang, T.: Multi-sphere support vector data description for brain-computer interface. In: 2012 Fourth International Conference on Communications and Electronics (ICCE), pp. 318–321 (2012)
Nguyen, P., Tran, D., Le, T., Huang, X., Ma, W.: EEG-Based Person Verification Using Multi-Sphere SVDD and UBM. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 289–300. Springer, Heidelberg (2013)
Nguyen, P., Tran, D., Huang, X., Sharma, D.: A Proposed Feature Extraction Method for EEG-based Person Identification. In: The International Conference on Artificial Intelligence (ICAI 2012), USA (2012)
Nguyen, P., Tran, D., Huang, X., Ma, W.: Motor Imagery EEG-Based Person Verification. In: Rojas, I., Joya, G., Cabestany, J. (eds.) IWANN 2013, Part II. LNCS, vol. 7903, pp. 430–438. Springer, Heidelberg (2013)
Palaniappan, R.: Two-stage biometric authentication method using thought activity brain waves. International Journal of Neural Systems 18 (2008)
Poulos, M., Rangoussi, M., Alexandris, N.: Neural network based person identification using EEG features. In: Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1999, pp. 1117–1120 (1999)
Poulos, M., Rangoussi, M., Alexandris, N., Evangelou, A.: Person identification from the EEG using nonlinear signal classification. Methods of Information in Medicine 41, 64–75 (2002)
Riera, A., Soria-Frisch, A., Caparrini, M., Grau, C., Ruffini, G.: Unobtrusive biometric system based on electroencephalogram analysis. EURASIP Journal on Advances in Signal Processing 2008 (2008)
Sanei, S., Chambers, J.: EEG signal processing. Wiley-Interscience (2007)
Yazdani, A., Roodaki, A., Rezatofighi, S.H., Misaghian, K., Setarehdan, S.K.: Fisher linear discriminant based person identification using visual evoked potentials. In: 9th International Conference on Signal Processing, ICSP 2008, pp. 1677–1680 (2008)
Welch, P.: The use of Fast Fourier Transform for the estimation of power spectra: a method based on time averaging over short, modified periodogram. IEEE Trans. Audio Electroacoustics 1967, 70–73 (1967)
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Pham, T., Ma, W., Tran, D., Nguyen, P., Phung, D. (2013). EEG-Based User Authentication in Multilevel Security Systems. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_46
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DOI: https://doi.org/10.1007/978-3-642-53917-6_46
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