Abstract
During the last decades, the biometric signal such as the face, fingerprints, and iris has been widely employed to identify the individual. Recently, electroencephalograms (EEG)-based user identification has received much attention. Up to now, many types of research have focused on deep learning-based approaches, which involves high storage, power, and computing resource. In this paper, a novel EEG-based user identification method is presented that can provide real-time and accurate recognition with a low computing resource. The main novelty is to describe the unique EEG pattern of an individual by fusing the temporal domain of single-channel features and channel-wise information. The channel-wise features are defined by symmetric matrices, the element of which is calculated by the Pearson correlation coefficient between two-pair channels. Channel-wise features are input to the multi-layer perceptron (MLP) for classification. To assess the verity of the proposed identification method, two well-known datasets were chosen, where the proposed method shows the best average accuracies of 98.55% and 99.84% on the EEGMMIDB and DEAP dataset, respectively. The experimental results demonstrate the superiority of the proposed method in modeling the unique pattern of an individual’s brainwave.
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References
Beijsterveldt, C., Boomsma, D.: Genetics of the human electroencephalogram (EEG) and event-related brain potentials (ERPs): a review. Hum. Genet. 94(4), 319–330 (1994)
Sun, Y., Lo, F.P.-W., Lo, B.: EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert Syst. Appl. 125, 259–267 (2019)
Mao, Z., Yao, W.X., Huang, Y.: EEG-based biometric identification with deep learning. In: 2017 8th International IEEE/EMBS Conference on Neural Engineering, pp. 609–612 (2017)
Phothisonothai, M.: An investigation of using SSVEP for EEG-based user authentication system. In: Proceedings of the IEEE Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 923–926 (2015)
Kumari, P., Vaish, A.: Brainwave based user identification system: a pilot study in robotics environment. Robot. Auton. Syst. 65, 15–23 (2015)
Koike-Akino, T., et al.: High-accuracy user identification using EEG biometrics. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 854–858 (2016)
Alyasseri, Z.A.A., Khader, A.T., Al-Betar, M.A., Papa, J.P., Alomari, O.A.: EEG feature extraction for person identification using wavelet decomposition and multi-objective flower pollination algorithm. IEEE Access 6, 76007–76024 (2018)
Goldberger, A., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
Koelstra, S., et al.: DEAP: a database for emotion analysis using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)
Candra, H., et al.: Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine. In: Proceedings of the 37th IEEE/EMBC, pp. 7250–7253 (2015)
Wikipedia page of Pearson correlation coefficient. https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
DEAP dataset. https://www.eecs.qmul.ac.uk/mmv/datasets/deap/readme.html
Fraschini, M., Hillebrand, A., Demuru, M., Didaci, L., Marcialis, G.: An EEG-based biometric system using eigenvector centrality in resting state brain networks. IEEE Signal Process. Lett. 22, 666–670 (2015)
EEG Motor Movement/Imagery Dataset. https://physionet.org/content/eegmmidb/1.0.0/
Acknowledgment
This research was supported by the MSIT (Ministry of Science, ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00465) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation). And this research was supported by the MISP (Ministry of Science, ICT & Future Planning), Korea, under the National Program for Excellence in SW (No. 2018-0-00213, Konkuk University) supervised by the IITP (Institute of Information & communications Technology Planing & Evaluation)” (No.2018-0-00213, Konkuk University). Finally the authors would like to express our deepest gratitude to Ms. Jayoung Yang for her helpful comments and discussions.
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Jin, L., Chang, J., Kim, E. (2020). EEG-Based User Identification Using Channel-Wise Features. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_58
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DOI: https://doi.org/10.1007/978-3-030-41299-9_58
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