Abstract
Biometric information extracted from electroencephalogram (EEG) signals is being used increasingly in person identification systems thanks to several advantages, compared to traditional ones such as fingerprint, face and voice. However, one of the major challenges is that a huge amount of EEG data needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. Although the lossy compression technique gives a higher Compression Ratio (CR) than lossless ones, they introduce the loss of information in recovered signals, which may affect to the performance of EEG-based person identification systems. In this paper, we investigate the impact of lossy compression on EEG data used in EEG-based person identification systems. Experimental results demonstrate that in the best case, CR could achieve up to 70 with minimal loss of person identification performance, and using EEG lossy compression is feasible compared to using lossless one.
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
References
Antoniol, G., Tonella, P.: EEG data compression techniques. IEEE Trans. Biomed. Eng. 44(2), 105–114 (1997)
Bao, X., Wang, J., Hu, J.: Method of individual identification based on electroencephalogram analysis. In: International Conference on New Trends in Information and Service Science, NISS 2009, pp. 390–393. IEEE (2009)
Begleiter, H.: EEG database. http://kdd.ics.uci.edu/databases/eeg/eeg.data.html
Brigham, K., Kumar, B.V.: Subject identification from electroencephalogram (EEG) signals during imagined speech. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1–8. IEEE (2010)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Dao, P.T., Li, X.J., Do, H.N.: Lossy compression techniques for EEG signals. In: 2015 International Conference on Advanced Technologies for Communications (ATC), pp. 154–159. IEEE (2015)
Daou, H., Labeau, F.: Performance analysis of a 2-D EEG compression algorithm using an automatic seizure detection system. In: 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), pp. 1632–1636. IEEE (2012)
Eyben, F., Wöllmer, M., Schuller, B.: OpenSMILE: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1459–1462. ACM (2010)
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Higgins, G., McGinley, B., Faul, S., McEvoy, R.P., Glavin, M., Marnane, W.P., Jones, E.: The effects of lossy compression on diagnostically relevant seizure information in EEG signals. IEEE J. Biomed. Health Inform. 17(1), 121–127 (2013)
Hill, D.: Value of the EEG in diagnosis of epilepsy. Br. Med. J. 1(5072), 663 (1958)
Marsan, C.A., Zivin, L.: Factors related to the occurrence of typical paroxysmal abnormalities in the EEG records of epileptic patients. Epilepsia 11(4), 361–381 (1970)
Nguyen, B., Nguyen, D., Ma, W., Tran, D.: Investigating the possibility of applying EEG lossy compression to EEG-based user authentication. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 79–85. IEEE (2017)
Nguyen, B., Nguyen, D., Ma, W., Tran, D.: Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3153–3160. IEEE (2017)
Nguyen, P., Tran, D., Huang, X., Sharma, D.: A proposed feature extraction method for EEG-based person identification. In: International Conference on Artificial Intelligence (2012)
Riera, A., Soria-Frisch, A., Caparrini, M., Grau, C., Ruffini, G.: Unobtrusive biometric system based on electroencephalogram analysis. EURASIP J. Adv. Signal Process. 2008, 18 (2008)
Said, A., Pearlman, W.A.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circuits Syst. Video Technol. 6(3), 243–250 (1996)
Shapiro, J.M.: Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Process. 41(12), 3445–3462 (1993)
Shedeed, H.A.: A new method for person identification in a biometric security system based on brain EEG signal processing. In: 2011 World Congress on Information and Communication Technologies (WICT), pp. 1205–1210. IEEE (2011)
Srinivasan, K., Reddy, M.R.: Efficient preprocessing technique for real-time lossless EEG compression. Electron. Lett. 46(1), 26–27 (2010)
Sun, S.: Multitask learning for EEG-based biometrics. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Nguyen, B., Ma, W., Tran, D. (2019). A Study of Combined Lossy Compression and Person Identification on EEG Signals. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-94120-2_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94119-6
Online ISBN: 978-3-319-94120-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)