Abstract
Electroencephalography (EEG) is the spontaneous and rhythmic electrical brain cell activity recorded by amplifying the biological potential from the scalp with precise electronic instruments. EEG highly satisfy the requirements of a biometric system such as uniqueness and circumvention, but when recording EEG signals, different events which subject performs, such as opening eyes, closing eyes, clenching fists and so on, will cause signal fluctuation. In this paper, we propose a EEG-based personal identification method. Linear-frequency cepstrum coefficients (LFCC) is used as input feature to get the frequency information from raw EEG signals. A gradient reverse layer (GRL) is added to eliminate the noise caused by different events. Personal identification task and event recognition task are trained together in an adversarial way to extract event-independent feature. Experimental results validate the effectiveness of the proposed method.
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
Robert Jenke, A.P., Buss, M.: Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affective Comput. 5(3), 327–339 (2014)
Picone, J.W.: Signal modeling techniques in speech recognition. Proc. IEEE 81(9), 1215–1247 (1993)
Ningjie, L., Yuchun, F., Ling, L., Limin, H., Fenglei, Y., Yike, G.: Multiple feature fusion for automatic emotion recognition using EEG signals. In: ICASSP IEEE International Conference on Acoustic Speech Signal Process Proceedings, pp. 896–900 (2018)
Li, Y., Zhao, Y., Tan, T., Liu, N., Fang, Y.: Personal identification based on content-independent EEG signal analysis. In: Zhou, J., et al. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 537–544. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69923-3_58
Thomas, K.P., Vinod, A.: Utilizing individual alpha frequency and delta band power in EEG based biometric recognition. In: IEEE International Conference on Systems, Man and Cybernetics, SMC - Conference Proceedings, pp. 4787–4791 (2016)
Keshishzadeh, S., Fallah, A., Rashidi, S.: Improved EEG based human authentication system on large dataset. In: Iranian Conference on Electrical Engineering, ICEE, pp. 1165–1169 (2016)
Xiang, Z., Lina, Y., Salil S., K., Yunhao, L., Tao, G., Kaixuan, C.: MindiD: person identification from brain waves through attention-based recurrent neural network. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(3) (2018)
Caruana, R.: Learning many related tasks at the same time with backpropagation. In: Advances in Neural Information Processing Systems, pp. 657–664 (1995)
Shinohara, Y.: Adversarial multi-task learning of deep neural networks for robust speech recognition. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, pp. 2369–2372 (2016)
Schalk, G., McFarland, D., Hinterberger, T., Birbaumer, N., Wolpaw, J.: BCI 2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)
Goldberger, A., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Thomas, K.P., Vinod, A.P.: EEG-based biometric authentication using gamma band power during rest state. Circ. Syst. Signal Process 37(1), 277–289 (2018)
Chuang, J., Nguyen, H., Wang, C., Johnson, B.: I think, therefore i am: usability and security of authentication using brainwaves. In: Adams, A.A., Brenner, M., Smith, M. (eds.) FC 2013. LNCS, vol. 7862, pp. 1–16. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41320-9_1
Acknowledgments
The work is supported by the National Natural Science Foundation of China under Grant No.: 61976132, U1811461 and the Natural Science Foundation of Shanghai under Grant No.: 19ZR1419200.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhou, M., Fang, Y., Xiao, Z. (2021). Personal Identification with Exploiting Competitive Tasks in EEG Signals. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-86608-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86607-5
Online ISBN: 978-3-030-86608-2
eBook Packages: Computer ScienceComputer Science (R0)