High Accuracy EEG Biometrics Identification Using ICA and AR Model

Authors

  • Chesada Kaewwit Advanced Virtual and Intelligent Computing Center Faculty of Science, Chulalongkorn University, Thailand
  • Chidchanok Lursinsap Advanced Virtual and Intelligent Computing Center Faculty of Science, Chulalongkorn University, Thailand
  • Peraphon Sophatsathit Advanced Virtual and Intelligent Computing Center Faculty of Science, Chulalongkorn University, Thailand

DOI:

https://doi.org/10.32890/jict2017.16.2.8

Keywords:

electroencephalogram (EEG), Autoregressive (AR), Independent Component Analysis (ICA), Biometrics, feature extraction, person classification

Abstract

Modern biometric identification methods combine interdisciplinary approaches to enhance person identification and classification accuracy. One popular technique for this purpose is Brain-Computer Interface (BCI). The signal so obtained from BCI will be further processed by the Autoregressive (AR) Model for feature extraction. Many researches in the area find that for more accurate results, the signal must be cleaned before extracting any useful feature information. This study proposes Independent Component Analysis (ICA), k-NN classifier, and AR as the combined techniques for electroencephalogram (EEG) biometrics to achieve the highest personal identification and classification accuracy. However, there is a classification gap between using the combined ICA with the AR model and AR model alone. Therefore, this study takes one step further by modifying the feature extraction of AR and comparing the outcome with the proposed approaches in lieu of prior researches. The experiment based on four relevant locations shows that the combined ICA and AR can achieve higher accuracy than the modified AR. More combinations of channels and subjects are required in future research to explore the significance of channel effects and to enhance the identification accuracy.

 

References

Albera, L., Kachenoura, A., Comon, P., Karfoul, A., Wendling, F., Senhadji, L., & Merlet, I. (2012). ICA-based EEG denoising: A comparative analysis of fifteen methods. Bull Pol Ac: Tech, 60(3), 407-418. doi:10.2478/ v10175-012-0052-3

Belouchrani, A., Abed-Meraim, K., Cardoso, J. F., & Moulines, E. (1997). A blind source separation technique using second-order statistics. IEEE Transactions on Signal Processing, 45(2), 434-444. doi:10.1109/78.554307

Belouchrani, A., & Cichocki, A. (2000). Robust whitening procedure in blind source separation context. Electronics Letters, 36(24), 2050-2051.

Bos, R., De Waele, S., & Broersen, P. M. T. (2002). Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data. IEEE Transactions on Instrumentation and Measurement, 51(6), 1289-1294. doi:10.1109/TIM.2002.808031 Journal of ICT, 16, No. 2 (Dec) 2017, pp: 354– Campisi, P., Scarano, G., Babiloni, F., DeVico Fallani, F., Colonnese,

S., Maiorana, E., & Forastiere, L. (2011). Brain waves based user recognition using the “eyes closed resting conditions” protocol. 2011 IEEE International Workshop on Information Forensics and Security,

WIFS 2011, 00(c), 16-19. doi:10.1109/WIFS.2011.6123138

Cardoso, J. F., & Souloumiac, A. (1993). Blind beamforming for non-gaussian signals (Vol. 140, pp. 362-362).

Cruces, S., Castedo, L., & Cichocki, A. (2002). Robust blind source separation algorithms using cumulants. Neurocomputing, 49, 87-118. doi:10.1016/ S0925-2312(02)00525-8 de Hoon, M. J. L., van der Hagen, T. H. J. J., Schoonewelle, H., & van Dam,

H. (1996). Why Yule-Walker should not be used for autoregressive modelling. Annals of Nuclear Energy, 23(15), 1219-1228. doi:http://dx.doi.org/10.1016/0306-4549(95)00126-3 Del Pozo-Banos, M., Alonso, J. B., Ticay-Rivas, J. R., & Travieso, C. M. (2014). Electroencephalogram subject identification: A review. Expert Systems with Applications, 41(15), 6537-6554. doi:10.1016/j. eswa.2014.05.013

Harshit, R. S., Thomas, K. P., Smitha, K. G., & Vinod, A. P. (2016). Online Electroencephalogram (EEG) based biometric authentication using visual and audio stimuli. The 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

Delorme, A., Palmer, J., Onton, J., Oostenveld, R., & Makeig, S. (2012). Independent EEG sources are dipolar. PLoS ONE, 7(2). doi:10.1371/ journal.pone.0030135

He, C., & Wang, Z. J. (2009). An independent component analysis (ICA) based approach for EEG person authentication. 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009(Dic), 1-4. doi:10.1109/ICBBE.2009.5162328

Hyvärinen, a., & Oja, E. (2000). Independent component analysis: Algorithms and applications. Neural networks : The Official Journal of the International Neural Network Society, 13(4-5), 411-430. doi:10.1016/ S0893-6080(00)00026-5 Journal of ICT, 16, No. 2 (Dec) 2017, pp: 354–

Kumari, P., & Vaish, A. (2016). Feature-level fusion of mental task’s brain signal for an efficient identification system. Neural Computing and Applications, 27(3), 659-669. doi:10.1007/s00521-015-1885-0

Maiorana, E., Rocca, D. L., Member, S., Campisi, P., & Member, S. (2016). On the permanence of EEG signals for biometric recognition. 11(1), 163-175. doi:10.1109/TIFS.2015.2481870

Mu, Z., Hu, J., & Min, J. (2016). EEG-based person authentication using a fuzzy entropy-related approach with two electrodes. Entropy, 18(12). doi:10.3390/e18120432

Paranjape, R. B., Mahovsky, J., Benedicenti, L., & Koles, Z. (2001). The electroencephalogram as a biometric. Canadian Conference on

Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555), 2, 1363-1366. doi:10.1109/CCECE.2001.933649

Poulos, M., Rangoussi, M., Chrissikopoulos, V., & Evangelou, A. (1999, 5-8 Sep 1999). Parametric person identification from the EEG using computational geometry. Proceedings of ICECS ‘99. The 6th IEEE International Conference on Electronics, Circuits and System.

Riera, A., Soria-Frisch, A., Caparrini, M., Grau, C., & Ruffini, G. (2008). Unobtrusive biometric system based on electroencephalogram analysis. Eurasip Journal on Advances in Signal Processing, 2008. doi:10.1155/2008/143728

Rocca, D. L., Campisi, P., & Scarano, G. (2012). EEG biometrics for individual recognition in resting state with closed eyes. International Conference of the Biometrics Special Interest Group (BIOSIG), 1-12. doi:10.1007/978-3-662-44485-6_22

Ruiz-Blondet, M. V., Jin, Z., & Laszlo, S. (2016). CEREBRE: A novel method for very accuracy event-related potential biometric identification> IEEE Transactions on Information Forensics and Security, 11 (7), 1618-1629. doi: 10.1109/TIFS.2016.2543524 Tangkraingkij, P., Lursinsap, C., Sanguansintukul, S., & Desudchit, T. (2010). Personal identification by EEG using ICA and neural network.

Computational Science and Its Applications–ICCSA 2010, 419-430. Journal of ICT, 16, No. 2 (Dec) 2017, pp: 354–

Tangkraingkij, P., Lursinsap, C., Sanguansintukul, S., & Desudchit, T. (2013). Insider and outsider person authentication with minimum number of brain wave signals by neural and homogeneous identity filtering. Neural Computing and Applications, 22(SUPPL.1), 463-476. doi:10.1007/ s00521-012-0974-6

Teplan, M. (2002). Fundamentals of EEG measurement. Measurement Science Review, 2, 1-11. doi:10.1021/pr070350l Yazdani, a., Roodaki, a., Rezatofighi, S. H., Misaghian, K., & Setarehdan, S.

K. (2008). Fisher linear discriminant based person identification using visual evoked potentials. 2008 9th International Conference on Signal Processing, 1677-1680. doi:10.1109/ICOSP.2008.4697459

Downloads

Published

06-11-2017

Issue

Vol. 16 No. 2 (2017): Journal of Information and Communication Technology (JICT) Vol.16, No.2, December 2017

Section

Articles

How to Cite

Kaewwit, C., Lursinsap, C., & Sophatsathit, P. (2017). High Accuracy EEG Biometrics Identification Using ICA and AR Model. Journal of Information and Communication Technology, 16(2), 354-373. https://doi.org/10.32890/jict2017.16.2.8

Research impact

Harvested 2026-09-06
12 citations, from OpenAlex — the highest of the sources checked

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/jict2017.16.2.8 OpenAlex W4245652480