EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey

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Tarik Bin Shams
Md. Sakir Hossain
Md. Firoz Mahmud
Md. Shahariar Tehjib
Zahid Hossain
Md. Ileas Pramanik

Abstract

An electroencephalogram (EEG) is a measurement that reflects the overall electrical activity in the brain. EEG signals are effective for biometric authentication and robust against malware attacks and any kind of fraud activities due to the uniqueness of the signals. Significant progress in research on EEG-based authentication has been achieved in the last few years, with machine learning being extensively used for classifying EEG signals. However, to the best of our knowledge, there has been no investigation into the overall progress made in such research. In this paper, the literature on the various factors involved in state-of-the-art biometric authentication systems is reviewed. We provide a thorough comparison of different machine learning biometric authentication techniques. The comparison criteria include the research objectives, machine learning algorithms, computational complexity, source of brainwaves, feature extraction methods, number of channels, and so on. Alongside the discussion of existing works, directions for future research are suggested to improve authentication accuracy. This paper provides an in-depth discussion of different advanced biometric authentication techniques, and a vivid picture of state-of-the-art machine learning-based biometric authentication techniques using EEG.

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How to Cite
Shams, T. B., Hossain, M. S., Mahmud, M. F., Tehjib, M. S., Hossain, Z., & Pramanik, M. I. (2022). EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 20(2), 225–241. https://doi.org/10.37936/ecti-eec.2022202.246906
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References

“Authentication Definition,” TechTerms.com. https://techterms.com/definition/authentication (accessed Jan. 4, 2021).

“What is ‘Authentication’,” The Economic Times. https://economictimes.indiatimes.com/definition/ authentication (accessed Jan. 4, 2021).

L. Norton et al., “Electroencephalographic recordings during withdrawal of life-sustaining therapy until 30 minutes after declaration of death,” Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques, vol. 44, no. 2, pp. 139–145, Oct. 2016.

“EEG (Electroencephalogram),” KidsHealth. https://kidshealth.org/en/parents/eeg.html (accessed Jan. 7, 2021).

Britannica, The Editors of Encyclopaedia. “electroencephalography,” Encyclopedia Britannica, Oct. 31, 2017. https://www.britannica.com/science/electroencephalography (accessed Jan. 4, 2021).

J. W. C. Medithe and U. R. Nelakuditi, “Study of normal and abnormal EEG,” in 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), 2016.

S. Siuly, Y. Li, and Y. Zhang, EEG Signal Analysis and Classification: Techniques and Applications. Cham, Switzerland: Springer, 2016, ch. 1, pp. 11–14.

A. S. Malik and H. U. Amin, Designing EEG Experiments for Studying the Brain: Design Code and Example Datasets. London, UK: Academic Press, 2017, ch. 1, p. 4.

M. L. Ali, J. V. Monaco, C. C. Tappert, and M. Qiu, “Keystroke biometric systems for user authentication,” Journal of Signal Processing Systems, vol. 86, pp. 175–190, Mar. 2016.

O. S. Adeoye, “A survey of emerging biometric technologies,” International Journal of Computer Applications, vol. 9, no. 10, pp. 1–5, Nov. 2010.

S. P. Banerjee and D. Woodard, “Biometric authentication and identification using keystroke dynamics: A survey,” Journal of Pattern Recognition Research, vol. 7, no. 1, pp. 116–139, 2012.

H. Crawford, “Keystroke dynamics: Characteristics and opportunities,” in 2010 Eighth International Conference on Privacy, Security and Trust, 2010, pp. 205–212.

M. Karnan, M. Akila, and N. Krishnaraj, “Biometric personal authentication using keystroke dynamics: A review,” Applied Soft Computing, vol. 11, no. 2, pp. 1565–1573, Mar. 2011.

R. Napier, W. Laverty, D. Mahar, R. Henderson, M. Hiron, and M. Wagner, “Keyboard user verification: toward an accurate, efficient, and ecologically valid algorithm,” International Journal of Human-Computer Studies, vol. 43, no. 2, pp. 213–222, Aug. 1995.

D. Shanmugapriya and G. Padmavathi, “A survey of biometric keystroke dynamics: Approaches, security and challenges,” International Journal of Computer Science and Information Security, vol. 5, no. 1, pp. 115–119, Sep. 2009.

Z. Rui and Z. Yan, “A survey on biometric authentication: Toward secure and privacy-preserving identification,” IEEE Access, vol. 7, pp. 5994–6009, 2019.

N. Ortiz, R. D. Hernandez, R. Jimenez, M. Mauledeoux, and O. Aviles, “Survey of biometric pattern recognition via machine learning techniques,” Contemporary Engineering Sciences, vol. 11, no. 34, pp. 1677–1694, 2018.

P. Harington, Machine Learning in Action. New York, USA: Manning Publication, 2012.

S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From Theory to Applications. New York, USA: Cambridge University Press, 2014, pp. 124–126.

T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, “How Many Trees in a Random Forest?,” in Machine Learning and Data Mining in Pattern Recognition, P. Perner, Ed. Berlin, Germany: Springer, 2012, pp. 154–168.

J. Brownlee, Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python. Machine Learning Mastery, 2019.

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning, 2nd ed. Cambridge, UK: The MIT Press, 2018, pp. 79–83.

Q. Gui, Z. Jin, and W. Xu, “Exploring EEG-based biometrics for user identification and authentication,” in 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2014.

M. K. Bashar, I. Chiaki, and H. Yoshida, “Human identification from brain EEG signals using advanced machine learning method EEG-based biometrics,” in 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016, pp. 475–479.

C. He and J. Wang, “An independent component analysis (ICA) based approach for EEG person authentication,” in 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009.

C. Ashby, A. Bhatia, F. Tenore, and J. Vogelstein, “Low-cost electroencephalogram (EEG) based authentication,” in 2011 5th International IEEE/EMBS Conference on Neural Engineering, 2011, pp. 442–445.

A. Lecko and Y. J. Sim, “Coefficient problems in the subclasses of close-to-star functions,” Results in Mathematics, vol. 74, May 2019, Art. no. 104.

Z. Mu, J. Hu, J. Min, and J. Yin, “Comparison of different entropies as features for person authentication based on EEG signals,” IET Biometrics, vol. 6, no. 6, pp. 409–417, Apr. 2017.

A. Rahman et al., “Multimodal EEG and keystroke dynamics based biometric system using machine learning algorithms,” IEEE Access, vol. 9, pp. 94625–94643, 2021.

L. A. Moctezuma and M. Molinas, “Event-related potential from EEG for a two-step identity authentication system,” in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019.

L. Moctezuma and M. Molinas, “Subject identification from low-density EEG-recordings of resting-states: A study of feature extraction and classification,” in Advances in Information and Communication, K. Arai and R. Bhatia, Eds. Cham, Switzerland: Springer, 2020, pp. 830–846.

B. Kaur and D. Singh, “Neuro signals: A future biomertic approach towards user identification,” in 7th International Conference on Cloud Computing, Data Science & Engineering – Confluence, 2017, pp. 112–117.

R. Palaniappan, “Method of identifying individuals using VEP signals and neural network,” IEE Proceedings - Science, Measurement and Technology, vol. 151, no. 1, pp. 16–20, Jan. 2004.

T. Yu, C.-S. Wei, K.-J. Chiang, M. Nakanishi, and T.-P. Jung, “EEG-based user authentication using a convolutional neural network,” in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019, pp. 1011–1014.

Z. Mao, W. X. Yao, and Y. Huang, “EEG-based biometric identification with deep learning,” in 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017, pp. 609–612.

J. Touryan, G. Apker, B. J. Lance, S. E. Kerick, A. J. Ries, and K. McDowell, “Estimating endogenous changes in task performance from EEG,” Frontiers in Neuroscience, vol. 8, Jun. 2014, Art. no. 155.

Y. Di, X. An, S. Liu, F. He, and D. Ming, “Using convolutional neural networks for identification based on EEG signals,” in 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2018, pp. 119–122.

T. Wilaiprasitporn, A. Ditthapron, K. Matchaparn, T. Tongbuasirilai, N. Banluesombatkul, and E. Chuangsuwanich, “Affective EEG-based person identification using the deep learning approach,” IEEE Transactions on Cognitive and Developmental Systems, vol. 12, no. 3, pp. 486–496, Sep. 2020.

S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Patras, “DEAP: A database for emotion analysis using physiological signals,” IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 18–31, Jan. 2012.

S. Puengdang, S. Tuarob, T. Sattabongkot, and B. Sakboonyarat, “EEG-based person authentication method using deep learning with visual stimulation,” in 11th International Conference on Knowledge and Smart Technology (KST), 2019, pp. 6–10.

U. Barayeu, N. Horlava, A. Libert, and M. V. Hulle, “Robust single-trial EEG-based authentication achieved with a 2-stage classifier,” Biosensors, vol. 10, no. 9, Sep. 2020, Art. no. 124.

S.-H. Liew, Y.-H. Choo, Y. F. Low, and Z. I. M. Yusoh, “EEG-based biometric authentication modelling using incremental fuzzy-rough nearest neighbour technique,” IET Biometrics, vol. 7, no. 2, pp. 145–152, Mar. 2018.

R. Palaniappan and D. P. Mandic, “Biometrics from brain electrical activity: A machine learning approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 738–742, Apr. 2007.

F. P. Sjamsudin, “EEG-based authentication with machine learning,” M.S. thesis, Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan, 2017.

T. Miyake, N. Kinjo, and I. Nakanishi, “Wavelet transform and machine learning-based biometric authentication using EEG evoked by invisible visual stimuli,” in IEEE Region 10 Conference (TENCON), 2020.

T. Pham, W. Ma, D. Tran, P. Nguyen, and D. Phung, “EEG-based user authentication in multilevel security systems,” in Advanced Data Mining and Applications, H. Motoda, Z. Wu, L. Cao, O. Zaiane, M. Yao, and W. Wang, Eds. Berlin, Germany: Springer, 2013, pp. 513–523.

V. Krishna, Y. Ding, A. Xu, and T. Höllerer, “Multimodal biometric authentication for VR/AR using EEG and eye tracking,” in 2019 International Conference on Multimodal Interaction, 2019.

R. Das, E. Maiorana, and P. Campisi, “Motor imagery for EEG biometrics using convolutional neural network,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 2062–2066.

P. Kasprowski, O. V. Komogortsev, and A. Karpov, “First eye movement verification and identification competition at BTAS 2012,” in 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2012.

S. Sun, “Multitask learning for EEG-based biometrics,” in 2008 19th International Conference on Pattern Recognition, 2008.

Y. Sun, F. P.-W. Lo, and B. Lo, “EEG-based user identification system using 1d-convolutional long short-term memory neural networks,” Expert Systems with Applications, vol. 125, pp. 259–267, Jul. 2019.

M. Wang, J. Hu, and H. Abbass, “Stable EEG biometrics using convolutional neural networks and functional connectivity,” Australian Journal of Intelligent Information Processing Systems, vol. 15, no. 3, pp. 19–26, 2019.

G. Schalk, D. McFarland, T. Hinterberger, N. Birbaumer, and J. Wolpaw, “BCI2000: A general-purpose brain-computer interface (BCI) system,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034–1043, Jun. 2004.

B. J. Edelman, B. Baxter, and B. He, “EEG source imaging enhances the decoding of complex righthand motor imagery tasks,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 1, pp. 4–14, Jan. 2016.

M. Zeynali and H. Seyedarabi, “EEG-based single-channel authentication systems with optimum electrode placement for different mental activities,” Biomedical Journal, vol. 42, no. 4, pp. 261–267, Aug. 2019.

T. Z. Chin, A. Saidatul, and Z. Ibrahim, “Exploring EEG based authentication for imaginary and non-imaginary tasks using power spectral density method,” IOP Conference Series: Materials Science and Engineering, vol. 557, 2019, Art. no. 012031.

A. Valsaraj, I. Madala, N. Garg, M. Patil, and V. Baths, “Motor imagery based multimodal biometric user authentication system using EEG,” in 2020 International Conference on Cyberworlds (CW), 2020.

T. Thorvaldsen, “A comparison of the least squares method and the Burg method for autoregressive spectral analysis,” IEEE Transactions on Antennas and Propagation, vol. 29, no. 4, pp. 675–679, Jul. 1981.

M. K. Abdullah, K. S. Subari, J. L. C. Loong, and N. N. Ahmad, “Analysis of the EEG signal for a practical biometric system,” International Journal of Biomedical and Biological Engineering, vol. 4, no. 8, pp. 364–368, 2010.

J. Sohankar, K. Sadeghi, A. Banerjee, and S. K. Gupta, “E-Bias: A pervasive EEG-based identification and authentication system,” in Proceedings of the 11th ACM Symposium on QoS and Security for Wireless and Mobile Networks, 2015, pp. 165–172.

D. H. Wolpert, “The lack of a priori distinctions between learning algorithms,” Neural Computation, vol. 8, no. 7, pp. 1341–1390, Oct. 1996.

J. Müller-Gerking, G. Pfurtscheller, and H. Flyvbjerg, “Designing optimal spatial filters for single-trial EEG classification in a movement task,” Clinical Neurophysiology, vol. 110, no. 5, pp. 787–798, May 1999.

P. Sajda, A. Gerson, K.-R. Muller, B. Blankertz, and L. Parra, “A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 184–185, Jun. 2003.

X. Bao, J. Wang, and J. Hu, “Method of individual identification based on electroencephalogram analysis,” in 2009 International Conference on New Trends in Information and Service Science, 2009, pp. 390–393.

J.-F. Hu, “Biometric system based on EEG signals by feature combination,” in 2010 International Conference on Measuring Technology and Mechatronics Automation, 2010, pp. 752–755.

X. Chen, Y. Wang, M. Nakanishi, X. Gao, T.-P. Jung, and S. Gao, “High-speed spelling with a noninvasive brain–computer interface,” Proceedings of the National Academy of Sciences, vol. 112, no. 44, pp. E6058–E6067, Oct. 2015.