A Biometric System Based on Single-channel EEG Recording in One-second

Full Text (PDF, 871KB), PP.28-40

Views: 0 Downloads: 0


Shaimaa Hagras 1,* Reham R. Mostafa 1 Ahmed Abou elfetouh 1

1. Information Systems Department, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2020.05.03

Received: 19 Nov. 2019 / Revised: 7 Jan. 2020 / Accepted: 26 Jan. 2020 / Published: 8 Oct. 2020

Index Terms

Biometrics, Electroencephalogram, Hjorth parameters, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Machine


In recent years, there are great research interests in using the Electroencephalogram (EEG) signals in biometrics applications. The strength of EEG signals as a biometric comes from its major fraud prevention capability. However, EEG signals are so sensitive, and many factors affect its usage as a biometric; two of these factors are the number of channels, and the required time for acquiring the signal; these factors affect the convenience and practicality. This study proposes a novel approach for EEG-based biometrics that optimizes the channels of acquiring data to only one channel. And the time to only one second. The results are compared against five commonly used classifiers named: KNN, Random Forest (RF), Support Vector Machine (SVM), Decision Tables (DT), and Naïve Bayes (NB). We test the approach on the public Texas data repository. The results prove the constancy of the approach for the eight minutes. The best result of the eyes-closed scenario is Average True Positive Rate (TPR) 99.1% and 98.2% for the eyes-opened. And it reaches 100% for multiple subjects.

Cite This Paper

Shaimaa Hagras, Reham R. Mostafa, Ahmed Abou elfetouh, "A Biometric System Based on Single-channel EEG Recording in One-second", International Journal of Intelligent Systems and Applications(IJISA), Vol.12, No.5, pp.28-40, 2020. DOI:10.5815/ijisa.2020.05.03


[2]M. Abo-Zahhad, S. M. Ahmed, and S. N. Abbas, “A new multi-level approach to EEG based human authentication using eye blinking,” Pattern Recognit. Lett., vol. 82, pp. 216–225, 2016.
[3]C. Rig Das, Maiorana, “EEG Biometrics Using Visual Stimuli : a Longitudinal Study,” IEEE Signal Process. Lett. Vol. Issue 3, vol. 9908, no. c, pp. 1–9, 2016.
[4]Z. A. Alkareem Alyasseri, A. T. Khader, M. A. Al-Betar, J. P. Papa, O. A. Alomari, and S. N. Makhadme, “An efficient optimization technique of EEG decomposition for user authentication system,” 2nd Int. Conf. BioSignal Anal. Process. Syst. ICBAPS 2018, pp. 1–6, 2018.
[5]L. A. Moctezuma, A. A. Torres-García, L. Villaseñor-Pineda, and M. Carrillo, “Subjects identification using EEG-recorded imagined speech,” Expert Syst. Appl., vol. 118, pp. 201–208, 2019.
[6]S. Marcel and J. del R. Millan, “Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 743–748, 2007.
[7]E. S. Haukipuro et al., “Mobile brainwaves: On the interchangeability of simple authentication tasks with low-cost, single-electrode EEG devices,” IEICE Trans. Commun., vol. E102B, no. 4, pp. 760–767, 2019.
[8]P. Campisi and D. La Rocca, “Brain waves for automatic biometric-based user recognition,” IEEE Trans. Inf. Forensics Secur., vol. 9, no. 5, pp. 782–800, 2014.
[9]R. Suppiah and A. P. Vinod, “Biometric identification using single channel EEG during relaxed resting state,” IET Biom., vol. 7, pp. 342–348, 2018.
[10]M. Hunter et al., “The Australian EEG Database,” Clin. EEG Neurosci., vol. 36, no. 2, pp. 76–81, 2005.
[11]“The CSU EEG database.” [Online]. Available: http://www.cs.colostate.edu/eeg/main/data/2011-12_BCI_at_CSU. [Accessed: 17-Jul-2019].
[12]“EEG During Mental Arithmetic Tasks.” [Online]. Available: https://physionet.org/physiobank/database/eegmat/[Accessed:17-Jul-2019].
[13]“EEG Motor Movement/Imagery Dataset.” [Online]. Available: https://physionet.org/physiobank/database/eegmmidb/ [Accessed:17-Jul-2019].
[14]“EEG Signals from an RSVP Task.” [Online]. Available: https://physionet.org/physiobank/database/ltrsvp/ [Accessed:17-Jul-019].
[15]“ERP-based Brain-Computer Interface recordings.” [Online]. Available: https://physionet.org/physiobank/database/erpbci/ [Accessed:17-Jul-019].
[16]“Evoked Auditory Responses in Normals across Stimulus Level.” [Online]. Available: https://physionet.org/physiobank/database/earndb/ [Accessed:17-Jul-019].
[17]“MAMEM Steady State Visually Evoked Potential EEG Database.”[Online].Available: https://physionet.org/physiobank/database/mssvepdb/ [Accessed:17-Jul-2019].
[18]“Deap dataset.”[Online]. Available: http://www.eecs.qmul.ac.uk/mmv/datasets/deap/index.html [Accessed:17-Jul-019].
[19]“The UCI EEG dataset.” [Online]. Available: https://archive.ics.uci.edu/ml/datasets/eeg%2Bdatabase [Accessed:17-Jul-2019].
[20]“Texas Data Repository.” [Online]. Available: https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/9TTLK8 [Accessed:17-Jul-2019].
[21]C. Brunner and R. Leeb, “Graz_Bci dataset A,” pp. 1–6, 2008.
[22]G. P. Clemens Brunner, Robert Leeb, Gernot Müller-Putz, Alois Schlögl, “BCI Competition 2008 – Graz data set B,” Knowl. Creat. Diffus. Util., pp. 1–6, 2008.
[23]“ATR dataset.” [Online]. Available: https://biomark00.atr.jp/modules/xoonips/listitem.php?indexid=181 [Accessed:17-Jul-019].
[24]“Keirn_and_Aunon dataset.” [Online]. Available: http://www.cs.colostate.edu/eeg/main/data/1989_Keirn_and_Aunon [Accessed:17-Jul-019].
[25]“Benci dataset.” [Online]. Available: http://bnci-horizon-2020.eu/database/data-sets [Accessed:17-Jul-019].
[26]G. Q. Zhu Dan, Zhou Xifeng, “An Identification System Based on Portable EEG Acquisition Equipment,” in Third International Conference on Intelligent System Design and Engineering Applications, 2013.
[27]Y. Bai, Z. Zhang, and D. Ming, “Feature selection and channel optimization for biometric identification based on visual evoked potentials,” in 19th International Conference on Digital Signal Processing, 2014, no. August, pp. 772–776.
[28]S. Keshishzadeh and A. Fallah, “Improved EEG based human authentication system on large dataset,” in Iranian Conference on Electrical Engineering (ICEE), 2016, pp. 1165–1169.
[29]L. A. Moctezuma, A. A. Torres-garc, L. Villase, L. A. Moctezuma, and A. A. Torres-garc, “Subjects Identification using EEG-recorded Imagined Speech,” Expert Syst. With Appl. Elsevier, 2018.
[30]Y. Sun, F. P. Lo, and B. Lo, “EEG-based user identification system using 1D-convolutional long short-term memory neural networks,” Expert Syst. Appl., 2019.
[31]S. Liu et al., “Individual Feature Extraction and Identification on EEG Signals in Relax and Visual Evoked Tasks,” in Biomedical Informatics and Technology, 2014, pp. 305–318.
[32]P. Nguyen, D. Tran, X. Huang, and D. Sharma, “A Proposed Feature Extraction Method for EEG-based Person Identification,” Int. Conf. Artif. Intell., 2012.
[33]S. K. Yeom, H. Il Suk, and S. W. Lee, “Person authentication from neural activity of face-specific visual self-representation,” Pattern Recognit., vol. 46, no. 4, pp. 1159–1169, 2013.
[34]Q. Gui, Z. Jin, and W. Xu, “Exploring EEG-based biometrics for user identification and authentication,” 2014 IEEE Signal Process. Med. Biol. Symp. IEEE SPMB 2014 - Proc., 2015.
[35]F. Su, L. Xia, A. Cai, Y. Wu, and J. Ma, “EEG-based personal identification: From proof-of-concept to a practical system,” Proc. - Int. Conf. Pattern Recognit., pp. 3728–3731, 2010.
[36]M. K. Abdullah, K. S. Subari, J. L. C. Loong, and N. N. Ahmad, “Analysis of effective channel placement for an EEG-based biometric system,” Proc. 2010 IEEE EMBS Conf. Biomed. Eng. Sci. IECBES 2010, no. December, pp. 303–306, 2010.
[37]B. Kaur, P. Kumar, P. P. Roy, and D. Singh, “Impact of Ageing on EEG based Biometric Systems,” 2017 4th IAPR Asian Conf. Pattern Recognit., pp. 459–464, 2017.
[38]W. Rahman and M. Gavrilova, “Overt Mental Stimuli of Brain Signal for Person Identification,” in International Conference on Cyberworlds, 2016, pp. 197–203.
[39]C. M. Issac and E. G. M. Kanaga, “Probing on Classification Algorithms and Features of Brain Signals Suitable for Cancelable Biometric Authentication,” 2017 IEEE Int. Conf. Comput. Intell. Comput. Res., pp. 1–4, 2017.
[40]K. Bashar, “ECG and EEG Based Multimodal Biometrics for Human Identification,” 2018 IEEE Int. Conf. Syst. Man, Cybern., pp. 4345–4350, 2018.
[41]J. Chuang, H. Nguyen, C. Wang, and B. Johnson, “I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves,” in Financial Cryptography and Data Security, 2013, pp. 1–16.
[42]M. T. Curran, J. K. Yang, N. Merrill, and J. Chuang, “Passthoughts authentication with low cost EarEEG,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2016-Octob, pp. 1979–1982, 2016.
[43]Z. Liang, S. Oba, and S. Ishii, “An Unsupervised EEG Decoding System for Human Emotion Recognition Acknowledgments This study was supported by the New Energy and Industrial Technology Development,” Neural Networks, 2019.
[44]A. Patil, “FEATURE EXTRACTION OF EEG FOR EMOTION RECOGNITION USING HJORTH FEATURES AND HIGHER ORDER CROSSINGS,” in Conference on Advances in Signal Processing (CASP) Cummins College of Engineering for Women, Pune., 2016, pp. 429–434.
[45]O. O. Kübra Eroğlu, Pınar Kurt, Temel Kayıkçıoğlu, “Investigation of Luminance Effect on the Emotional Assessment Using Hjorth Descriptors,” in Medical Technologies National Congress (TIPTEKNO), 2016
[46]M. Tanveer, “Classification of seizure and seizure-free EEG signals using Hjorth parameters,” in 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018, pp. 2180–2185.
[47]R. M. Mehmood, R. Du, and H. J. Lee, “Optimal feature selection and deep learning ensembles method for emotion recognition from human brain EEG sensors,” IEEE Access, vol. 5, pp. 14797–14806, 2017.
[48]S.-H. Liew, Y.-H. Choo, Y. F. Low, and Z. I. Mohd Yusoh, “EEG-based biometric authentication modelling using incremental fuzzy-rough nearest neighbour technique,” IET Biometrics, vol. 7, no. 2, pp. 145–152, 2017.
[49]E. Frank, M. A. Hall, and I. H. Witten, “The WEKA Workbench Data Mining: Practical Machine Learning Tools and Techniques,” Morgan Kaufmann, Fourth Ed., p. 128, 2016.
[50]A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” vol. 134, pp. 9–21, 2004.
[51]POWERS, D.M.W., " EVALUATION: FROM PRRECISION, RECALL AND F-MEASURE TO ROC, INFORMEDNESS, MARKEDNESS & CORRELATION", Journal of Machine Learning Technologies, Volume 2, Issue 1, 2011, pp-37-63