ABSTRACT
Electroencephalography (EEG) is the record of electrogram of the electrical activity on the scalp typically using non-invasive electrodes. In recent years, many studies started using EEG as a human characteristic to construct biometric identification or authentication. Being a kind of behavioral characteristics, EEG has its natural advantages whereas some characteristics have not been fully evaluated. For instance, we find that Motor Imagery (MI) brain-computer interface is mainly used for improving neurological motor function, but has not been widely studied in EEG authentication. Currently, there are many mature methods for understanding such signals. In this paper, we propose an enhanced EEG authentication framework with Motor Imagery, by offering a complete EEG signal processing and identity verification. Our framework integrates signal preprocess, channel selection and deep learning classification to provide an end-to-end authentication. In the evaluation, we explore the requirements of a biometric system such as uniqueness, permanency, collectability, and investigate the framework regarding insider and outsider attack performance, cross-session performance, and influence of channel selection. We also provide a large comparison with state-of-the-art methods, and our experimental results indicate that our framework can provide better performance based on two public datasets.
- Pierre Ablin, Jean Francois Cardoso, and Alexandre Gramfort. 2018. Faster ICA under Orthogonal Constraint. Icassp, Ieee International Conference on Acoustics, Speech and Signal Processing - Proceedings 2018- (2018), 4464–4468.Google Scholar
- Pierre Ablin, Jean Francois Cardoso, and Alexandre Gramfort. 2018. Faster independent component analysis by preconditioning with hessian approximations. Ieee Transactions on Signal Processing 66, 15 (2018), 4040–4049.Google ScholarDigital Library
- Ali Al-Saegh, Shefa A. Dawwd, and Jassim M. Abdul-Jabbar. 2021. Deep learning for motor imagery EEG-based classification: A review. Biomedical Signal Processing and Control 63 (2021), 102172.Google ScholarCross Ref
- Zaid Abdi Alkareem Alyasseri, Osama Ahmad Alomari, Sharif Naser Makhadmeh, Seyedali Mirjalili, Mohammed Azmi Al-Betar, Salwani Abdullah, Nabeel Salih Ali, Joao P. Papa, Douglas Rodrigues, and Ammar Kamal Abasi. 2022. EEG Channel Selection for Person Identification Using Binary Grey Wolf Optimizer. Ieee Access 10(2022), 10500–10513.Google ScholarCross Ref
- Zaid Abdi Alkareem Alyasseri, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, João P. Papa, and Osama Ahmad Alomari. 2018. EEG-based Person Authentication Using Multi-objective Flower Pollination Algorithm. 2018 Ieee Congress on Evolutionary Computation, Cec 2018 - Proceedings (2018), 8477895.Google Scholar
- Kai Keng Ang, Zheng Yang Chin, Haihong Zhang, and Cuntai Guan. 2008. Filter Bank Common Spatial Pattern (FBCSP) in brain-computer interface. Proceedings of the International Joint Conference on Neural Networks (2008), 2390–2397.Google Scholar
- Blair C. Armstrong, Maria V. Ruiz-Blondet, Negin Khalifian, Kenneth J. Kurtz, Zhanpeng Jin, and Sarah Laszlo. 2015. Brainprint: Assessing the uniqueness, collectability, and permanence of a novel method for ERP biometrics. Neurocomputing 166(2015), 59–67.Google ScholarDigital Library
- Corey Ashby, Amit Bhatia, Francesco Tenore, and Jacob Vogelstein. 2011. Low-cost electroencephalogram (EEG) based authentication. 2011 5th International Ieee/embs Conference on Neural Engineering, Ner 2011 (2011), 442–445.Google ScholarCross Ref
- Luiz A. Baccalá and Koichi Sameshima. 2001. Partial directed coherence: A new concept in neural structure determination. Biological Cybernetics 84, 6 (2001), 463–474.Google ScholarCross Ref
- D Baldisserra, A Franco, D Maio, and D Maltoni. 2006. Fake fingerprint detection by odor analysis. Advances in Biometrics, Proceedings 3832 (2006), 265–272.Google Scholar
- Wei-Yang Chiu, Weizhi Meng, and Wenjuan Li. 2021. I Can Think Like You! Towards Reaction Spoofing Attack on Brainwave-Based Authentication. Lecture Notes in Computer Science 12382 (2021), 251–265.Google ScholarDigital Library
- John Chuang, Hamilton Nguyen, Charles Wang, and Benjamin Johnson. 2013. I think, therefore I am: Usability and security of authentication using brainwaves. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7862 (2013), 1–16.Google Scholar
- Nguyen T.K. Cuong, Vo Q. Ha, Nguyen T.M. Huong, Truong Quang Dang Khoa, Nguyen Huynh Minh Tam, Huynh Q. Linh, and Vo Van Toi. 2010. Removing noise and artifacts from EEG using adaptive noise cancelator and blind source separation. Ifmbe Proceedings 27(2010), 282–286.Google ScholarCross Ref
- Rig Das, Emanuele Maiorana, and Patrizio Campisi. 2016. EEG Biometrics Using Visual Stimuli: A Longitudinal Study. Ieee Signal Processing Letters 23, 3 (2016), 341–345.Google ScholarCross Ref
- Rig Das, Emanuele Maiorana, and Patrizio Campisi. 2018. MOTOR IMAGERY FOR EEG BIOMETRICS USING CONVOLUTIONAL NEURAL NETWORK. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (icassp) (2018), 2062–2066.Google Scholar
- Yang Di, Xingwei An, Feng He, Shuang Liu, Yufeng Ke, and Dong Ming. 2019. Robustness Analysis of Identification Using Resting-State EEG Signals. Ieee Access 7(2019), 42113–42122.Google ScholarCross Ref
- Nesli Erdogmus and Sebastien Marcel. 2013. Spoofing 2D face recognition systems with 3D masks. Lecture Notes in Informatics (lni), Proceedings - Series of the Gesellschaft Fur Informatik (gi) P-212 (2013), 6617158.Google Scholar
- David Feess, Mario M. Krell, and Jan H. Metzen. 2013. Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface. Plos One 8, 7 (2013), e67543.Google ScholarCross Ref
- 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. 2000. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, 23 (2000), E215–220.Google ScholarCross Ref
- C. W. J. Granger. 1969. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica 37, 3 (1969), 424.Google ScholarCross Ref
- Qiong Gui, Zhanpeng Jin, Wenyao Xu, Maria V. Ruiz-Blondet, and Sarah Laszlo. 2015. Multichannel EEG-based biometric using improved RBF neural networks. 2015 Ieee Signal Processing in Medicine and Biology Symposium - Proceedings (2015), 7405418.Google ScholarCross Ref
- Thorir Mar Ingolfsson, Michael Hersche, Xiaying Wang, Nobuaki Kobayashi, Lukas Cavigelli, and Luca Benini. 2020. EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces. Conference Proceedings - Ieee International Conference on Systems, Man and Cybernetics 2020-(2020), 2958–2965.Google Scholar
- Md Kafiul Islam, Amir Rastegarnia, and Zhi Yang. 2016. Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique 46, 4-5 (2016), 287–305.Google ScholarCross Ref
- Amir Jalaly Bidgoly, Hamed Jalaly Bidgoly, and Zeynab Arezoumand. 2020. A survey on methods and challenges in EEG based authentication. Computers and Security 93 (2020), 101788.Google ScholarCross Ref
- Isuru Jayarathne, Michael Cohen, and Senaka Amarakeerthi. 2016. BrainID: Development of an EEG-based biometric authentication system. 7th Ieee Annual Information Technology, Electronics and Mobile Communication Conference, Ieee Iemcon 2016(2016), 7746325.Google Scholar
- Donghyeon Kim and Kiseon Kim. 2019. Resting State EEG-Based Biometric System Using Concatenation of Quadrantal Functional Networks. Ieee Access 7(2019), 65745–65756.Google ScholarCross Ref
- Pradeep Kumar, Rajkumar Saini, Partha Pratim Roy, and Debi Prosad Dogra. 2017. A bio-signal based framework to secure mobile devices. Journal of Network and Computer Applications 89 (2017), 62–71.Google ScholarDigital Library
- Pinki Kumari Sharma and Abhishek Vaish. 2016. Individual identification based on neuro-signal using motor movement and imaginary cognitive process. Optik 127, 4 (2016), 2143–2148.Google ScholarCross Ref
- Vernon J. Lawhern, Amelia J. Solon, Nicholas R. Waytowich, Stephen M. Gordon, Chou P. Hung, and Brent J. Lance. 2018. EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces. Journal of Neural Engineering 15, 5 (2018), 056013.Google ScholarCross Ref
- Yang Li, Xian Rui Zhang, Bin Zhang, Meng Ying Lei, Wei Gang Cui, and Yu Zhu Guo. 2019. A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding. Ieee Transactions on Neural Systems and Rehabilitation Engineering 27, 6(2019), 1170–1180.Google ScholarCross Ref
- Ruhi Mahajan and Bashir I. Morshed. 2015. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. Ieee Journal of Biomedical and Health Informatics 19, 1(2015), 158–165.Google ScholarCross Ref
- Emanuele Maiorana and Patrizio Campisi. 2018. Longitudinal Evaluation of EEG-Based Biometric Recognition. Ieee Transactions on Information Forensics and Security 13, 5(2018), 1123–1138.Google ScholarDigital Library
- Ravikiran Mane, Effie Chew, Karen Chua, Kai Keng Ang, Neethu Robinson, A. P. Vinod, Seong-Whan Lee, and Cuntai Guan. 2021. FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface. (2021).Google Scholar
- Sébatien Marcel and José del R. Millan. 2007. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 4(2007), 743–748.Google ScholarDigital Library
- Orlando Nieves and Vidya Manian. 2016. Automatic person authentication using fewer channel EEG motor imagery. World Automation Congress Proceedings 2016- (2016), 7582945.Google ScholarCross Ref
- Ibrahim Omerhodzic, Samir Avdakovic, Amir Nuhanovic, and Kemal Dizdarevic. 2013. Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier. (2013).Google Scholar
- Ozan Özdenizci, Ye Wang, Toshiaki Koike-Akino, and Deniz Erdoğmuş. 2019. Adversarial deep learning in EEG biometrics. IEEE signal processing letters 26, 5 (2019), 710–714.Google ScholarCross Ref
- Katya Pivcevic. 2021. Smart city growth creates biometrics opportunity. https://www.biometricupdate.com/202104/smart-city-growth-creates-biometrics-opportunity. [Online; accessed 30-April-2022].Google Scholar
- M. Poulos, M. Rangoussi, and E. Kafetzopoulos. 1998. Person identification via the EEG using computational geometry algorithms. 9th European Signal Processing Conference (eusipco 1998) (1998), 4 pp.Google Scholar
- W Rief. 2006. Getting started with neurofeedback. Journal of Psychosomatic Research 60, 3 (2006), 313–313.Google ScholarCross Ref
- Koosha Sadeghi, Ayan Banerjee, Javad Sohankar, and Sandeep K.S. Gupta. 2017. Geometrical analysis of machine learning security in biometric authentication systems. Proceedings - 16th Ieee International Conference on Machine Learning and Applications, Icmla 2017 2017- (2017), 309–314. https://doi.org/10.1109/ICMLA.2017.0-142Google ScholarCross Ref
- Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Paul Cumming, and Masahiro Iwahashi. 2018. Automated classification and removal of EEG artifacts with SVM and wavelet-ICA. Ieee Journal of Biomedical and Health Informatics 22, 3(2018), 664–670.Google ScholarCross Ref
- Nima Salimi, Michael Barlow, and Erandi Lakshika. 2020. Towards Potential of N-back Task as Protocol and EEGNet for the EEG-based Biometric. 2020 Ieee Symposium Series on Computational Intelligence, Ssci 2020 (2020), 1718–1724.Google Scholar
- Gerwin Schalk, Dennis J. McFarland, Thilo Hinterberger, Niels Birbaumer, and Jonathan R. Wolpaw. 2004. BCI2000: A general-purpose brain-computer interface (BCI) system. Ieee Transactions on Biomedical Engineering 51, 6 (2004), 1034–1043.Google ScholarCross Ref
- Ioannis Stylios, Spyros Kokolakis, Olga Thanou, and Sotirios Chatzis. 2021. Behavioral biometrics & continuous user authentication on mobile devices: A survey. Inf. Fusion 66(2021), 76–99.Google ScholarCross Ref
- Jiayao Sun, Jin Xie, and Huihui Zhou. 2021. EEG classification with transformer-based models. Lifetech 2021 - 2021 Ieee 3rd Global Conference on Life Sciences and Technologies (2021), 92–93.Google Scholar
- Yingnan Sun, Frank P.W. Lo, and Benny Lo. 2019. EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert Systems With Applications 125 (2019), 259–267.Google ScholarDigital Library
- Yousef Rezaei Tabar and Ugur Halici. 2016. A novel deep learning approach for classification of EEG motor imagery signals. Journal of neural engineering 14, 1 (2016), 016003.Google ScholarCross Ref
- Preecha Tangkraingkij, Chidchanok Lursinsap, Siripun Sanguansintukul, and Tayard Desudchit. 2010. Personal identification by EEG using ICA and neural network. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6018, 3(2010), 419–430.Google Scholar
- Hesam Varsehi and S. Mohammad P. Firoozabadi. 2021. An EEG channel selection method for motor imagery based brain computer interface and neurofeedback using Granger causality. Neural Networks 133(2021), 193–206.Google ScholarCross Ref
- Scott I. Vrieze. 2012. Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods 17, 2 (2012), 228–243.Google ScholarCross Ref
- Mei Wang and Weihong Deng. 2021. Deep face recognition: A survey. Neurocomputing 429(2021), 215–244.Google ScholarCross Ref
- Irene Winkler, Stefan Haufe, and Michael Tangermann. 2011. Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals. Behavioral and Brain Functions 7, 1 (2011), 30.Google ScholarCross Ref
- Jianwei Yang, Zhen Lei, and Stan Z. Li. 2014. Learn Convolutional Neural Network for Face Anti-Spoofing. (2014), 8.Google Scholar
- Zitong Yu, Chenxu Zhao, Zezheng Wang, Yunxiao Qin, Zhuo Su, Xiaobai Li, Feng Zhou, and Guoying Zhao. 2020. Searching Central Difference Convolutional Networks for Face Anti-Spoofing. (2020).Google Scholar
- Ying Zeng, Qunjian Wu, Kai Yang, Li Tong, Bin Yan, Jun Shu, and Dezhong Yao. 2019. EEG-Based Identity Authentication Framework Using Face Rapid Serial Visual Presentation with Optimized Channels. Sensors 19, 1 (2019).Google Scholar
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- Towards Enhanced EEG-based Authentication with Motor Imagery Brain-Computer Interface
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