Comparative Performance Analysis of Machine and Deep Learning Models for EEG-Based Biometric Authentication
Abstract
EEG-based biometric authentication has emerged as a secure alternative to conventional authentication
methods, owing to its resistance to spoofing and inherent movement/image individual variability. This study
evaluated the performance of various classification models in the EEG motor movement/image dataset,
which comprises 1,526 sessions recorded from 109 subjects using 64 EEG channels at a sampling rate of
160 Hz. A comprehensive set of 1,600 features per session was extracted in the time, frequency, and timefrequency
domains. Following standard pre-processing and normalization, the models were trained in a
stratified 70/30 training test split using features standardized to zero mean and unit variance.
We systematically compared traditional machine learning classifiers, ensemble methods, and deep learning
architectures. Hyperparameter tuning was performed uniformly across all the models. The Ridge Classifier
achieved the highest accuracy (93.8%), followed by Logistic Regression (91.27%) and MLP (89.96%),
demonstrating the strength of linear and shallow neural models on engineered EEG features. In contrast,
deep learning models, including CNN, LSTM, GRU, and BiLSTM, recorded significantly lower accuracy
( 0.87%) because of limited training data and the use of pre-extracted statistical features instead of raw
time-series input, which restricted their ability to learn temporal patterns.
These findings indicate that traditional machine-learning models, when applied to well-crafted features,
remain highly competitive for EEG-based authentication. They offer a favorable balance between performance,
computational efficiency, and interpretability, whereas deep learning approaches require further
adaptation to the structure and scale of EEG data.
Full Text:
PDFReferences
R. Hartanto, A. Susanto, and P. I. Santosa, “Real time static hand gesture recognition system prototype for indonesian sign language,” in 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2014, pp. 1–6.
N. K. Ratha, J. H. Connell, and R. M. Bolle, “Enhancing security and privacy in biometrics-based authentication systems,” IBM systems Journal, vol. 40, no. 3, pp. 614–634, 2001.
S. Marcel and J. d. R. Milla´n, “Person authentication using brainwaves (eeg) and maximum a posteriori model adaptation,” IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 4, pp. 743–752, 2007.
M. DelPozo-Banos, C. M. Travieso, C. T. Weidemann, and J. B. Alonso, “Eeg biometric identification: a thorough exploration of the time-frequency domain,” Journal of neural engineering, vol. 12, no. 5, p. 056019, 2015.
G.-C. Yang, “Next-generation personal authentication scheme based on eeg signal and deep learning,” Journal of Information Processing Systems, vol. 16, no. 5, pp. 1034–1047, 2020.
S. Zhang, L. Sun, X. Mao, C. Hu, and P. Liu, “Review on eeg-based authentication technology,” Computational intelligence and neuroscience, vol. 2021, no. 1, p. 5229576, 2021.
K. P. Thomas and A. P. Vinod, “Eeg-based biometric authentication using gamma band power during rest state,” Circuits, Systems, and Signal Processing, vol. 37, no. 1, pp. 277–289, 2018.
S. Amlani, S. Jaiswal, and S. Patil, “Session authentication using color scheme,” Proc. Int. J. Comput. Sci. Inf. Technol.(IJCSIT), vol. 6, no. 2, pp. 1420–1423, 2015.
C. Priya, “Behavioral biometrics based authentication system using mlp-nn and mvpa,” in 2021 IEEE International Power and Renewable Energy Conference (IPRECON). IEEE, 2021, pp. 1–6.
Y. S. Soni, S. Somani, and V. Shete, “Biometric user authentication using brain waves,” in 2016 International Conference on Inventive Computation Technologies (ICICT), vol. 2. IEEE, 2016, pp. 1–6.
K. Lis, E. Niewiadomska-Szynkiewicz, and
K. Dziewulska, “Siamese neural network for keystroke dynamics-based authentication on partial passwords,” Sensors, vol. 23, no. 15, p. 6685, 2023.
M. M. Pandi and A. Valarmathi, “A secured graphical password authentication system,” International Journal of Engineering Research & Technology (IJERT), vol. 2, no. 5, pp. 1013–1019, 2013.
H. Om and M. R. Reddy, “Geometric based remote password authentication using biometrics,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 16, no. 4-5, pp. 207–220, 2013.
A. Deshpande and R. Joshi, “Information security using cryptography and image processing,” IJSRDInternational Journal for Scientific Research & Development, vol. 1, no. 9, 2013.
W. Khalifa, A. Salem, M. Roushdy, and K. Revett, “A survey of eeg based user authentication schemes,” in 2012 8th International Conference on Informatics and Systems (INFOS). IEEE, 2012, pp. BIO–55.
Z. Ni, A. C. Yuksel, X. Ni, M. I. Mandel, and L. Xie, “Confused or not confused? disentangling brain activity from eeg data using bidirectional lstm recurrent neural networks,” in Proceedings of the 8th acm international conference on bioinformatics, computational biology, and health informatics, 2017, pp. 241–246.
P. Wang and J. Hu, “A hybrid model for eegbased gender recognition,” Cognitive neurodynamics, vol. 13, no. 6, pp. 541–554, 2019.
S. M. Alarcao and M. J. Fonseca, “Emotions recognition using eeg signals: A survey,” IEEE transactions on affective computing, vol. 10, no. 3, pp. 374–393, 2017.
Y. Ho¨ller and A. Uhl, “Do eeg-biometric templates threaten user privacy?” in Proceedings of the 6th ACM workshop on information hiding and multimedia security, 2018, pp. 31–42.
S. Mueller, D. Wang, M. D. Fox, B. T. Yeo, J. Sepulcre, M. R. Sabuncu, R. Shafee, J. Lu, and H. Liu,
“Individual variability in functional connectivity architecture of the human brain,” Neuron, vol. 77, no. 3, pp. 586–595, 2013.
M. Wang, J. Hu, and H. A. Abbass, “Brainprint: Eeg biometric identification based on analyzing brain connectivity graphs,” Pattern Recognition, vol. 105, p. 107381, 2020.
J. Galbally, S. Marcel, and J. Fierrez, “Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition,” IEEE transactions on image processing, vol. 23, no. 2, pp. 710–724, 2013.
J. Bonneau, C. Herley, P. C. Van Oorschot, and F. Stajano, “The quest to replace passwords: A framework for comparative evaluation of web authentication schemes,” in 2012 IEEE symposium on security and privacy. IEEE, 2012, pp. 553–567.
J. J. Bengson, T. A. Kelley, X. Zhang, J.-L. Wang, and G. R. Mangun, “Spontaneous neural fluctuations predict decisions to attend,” Journal of Cognitive Neuroscience, vol. 26, no. 11, pp. 2578–2584, 2014.
G. Schalk, D. J. McFarland, T. Hinterberger, N. Birbaumer, and J. R. Wolpaw, “Bci2000: a generalpurpose brain-computer interface (bci) system,” IEEE Transactions on biomedical engineering, vol. 51, no. 6, pp. 1034–1043, 2004.
M. Ruiz-Blondet, N. Khlaifian, B. Armstrong, Z. Jin, K. Kurtz, and S. Laszlo, “Brainprint: Identifying unique features of neural activity with machine learning,” in Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 36, no. 36, 2014.
X. Jin, J. Tang, X. Kong, Y. Peng, J. Cao, Q. Zhao, and W. Kong, “Ctnn: A convolutional tensor-train neural network for multi-task brainprint recognition,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 103–112, 2020.
S. B. Salem and Z. Lachiri, “Cnn-svm approach for eeg-based person identification using emotional dataset,” in 2019 International Conference on Signal, Control and Communication (SCC). IEEE, 2019, pp. 241–245.
S. Altahat, G. Chetty, D. Tran, and W. Ma,
“Analysing the robust eeg channel set for person authentication,” in International conference on neural information processing. Springer, 2015, pp. 162–173.
Q. Wu, Y. Zeng, C. Zhang, L. Tong, and B. Yan, “An eeg-based person authentication system with open-set capability combining eye blinking signals,” Sensors, vol. 18, no. 2, p. 335, 2018.
X. Zhang, L. Yao, C. Huang, T. Gu, Z. Yang, and Y. Liu, “Deepkey: A multimodal biometric authentication system via deep decoding gaits and brainwaves,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 11, no. 4, pp. 1–24, 2020.
J. Li, Z. Zhang, and H. He, “Implementation of eeg emotion recognition system based on hierarchical convolutional neural networks,” in Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016, Beijing, China, November 28-30, 2016, Proceedings 8. Springer, 2016, pp. 22–33.
G. Rodriguez-Bermudez and P. J. Garcia-Laencina, “Analysis of eeg signals using nonlinear dynamics and chaos: a review,” Applied mathematics & information sciences, vol. 9, no. 5, p. 2309, 2015.
P. Kumar, R. Saini, B. Kaur, P. P. Roy, and
E. Scheme, “Fusion of neuro-signals and dynamic signatures for person authentication,” Sensors, vol. 19, no. 21, p. 4641, 2019.
D. Nguyen, D. Tran, D. Sharma, and W. Ma, “On the study of eeg-based cryptographic key generation,” Procedia computer science, vol. 112, pp. 936–945, 2017.
P. Campisi and D. La Rocca, “Brain waves for automatic biometric-based user recognition,” IEEE transactions on information forensics and security, vol. 9, no. 5, pp. 782–800, 2014.
M. L. Martini, E. K. Oermann, N. L. Opie, F. Panov, T. Oxley, and K. Yaeger, “Sensor modalities for braincomputer interface technology: a comprehensive literature review,” Neurosurgery, vol. 86, no. 2, pp. E108– E117, 2020.
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, 2007.
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. IEEE, 2011, pp. 442–445.
J. Malmivuo, “Comparison of the properties of eeg and meg in detecting the electric activity of the brain,” Brain topography, vol. 25, pp. 1–19, 2012.
E. Gupta, M. Agarwal, and R. Sivakumar, “Blink to get in: Biometric authentication for mobile devices using eeg signals,” in ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE, 2020, pp. 1–6.
S. R. K. Gopal and D. Shukla, “Concealable biometric-based continuous user authentication system an eeg induced deep learning model,” in 2021 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2021, pp. 1–8.
R. Das, E. Maiorana, and P. Campisi, “Motor imagery for eeg biometrics using convolutional neural network,” in 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2018, pp. 2062–2066.
T. Koike-Akino, R. Mahajan, T. K. Marks, Y. Wang, S. Watanabe, O. Tuzel, and P. Orlik, “High-accuracy user identification using eeg biometrics,” in 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, 2016, pp. 854–858.
H. Huang, L. Hu, F. Xiao, A. Du, N. Ye, and F. He, “An eeg-based identity authentication system with audiovisual paradigm in iot,” Sensors, vol. 19, no. 7, p. 1664, 2019.
S. Keshishzadeh, A. Fallah, and S. Rashidi, “Improved eeg based human authentication system on large dataset,” in 2016 24th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2016, pp. 1165–1169.
Q. Gui, M. V. Ruiz-Blondet, S. Laszlo, and Z. Jin, “A survey on brain biometrics,” ACM Computing Surveys (CSUR), vol. 51, no. 6, pp. 1–38, 2019.
A. Mishra, N. Raj, and G. Bajwa, “Eeg-based image feature extraction for visual classification using deep learning,” in 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA). IEEE, 2022, pp. 181–188.
J. Y. Cheng, H. Goh, K. Dogrusoz, O. Tuzel, and E. Azemi, “Subject-aware contrastive learning for biosignals,” arXiv preprint arXiv:2007.04871, 2020.
B. Bandana Das, S. Kumar Ram, K. Sathya Babu, R. K. Mohapatra, and S. P. Mohanty, “Person identification using autoencoder-cnn approach with multitask-based eeg biometric,” Multimedia Tools and Applications, vol. 83, no. 35, pp. 83205–83225, 2024.
A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley, “Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals,” circulation, vol. 101, no. 23, pp. e215–e220, 2000.
DOI: https://doi.org/10.31449/inf.v49i8.9228
This work is licensed under a Creative Commons Attribution 3.0 License.