Review on identity feature extraction methods based on electroencephalogram signals_Journal of Biomedical Engineering

Authors：

ZHONG Wenxiao ^1,2 ,  AN Xingwei ¹ , DI Yang ¹ , ZHANG Lixin ^1,2 , MING Dong ^1,2

1. Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China;
2. Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, TianJin 300072, P.R.China;

Corresponding author：

AN Xingwei, Email: anxingwei@tju.edu.cn

Keywords：

electroencephalogram; identity identification; feature extraction; single-channel features; inter-channel features

DOI：

10.7507/1001-5515.202102057

Video：

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Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.

Citation： ZHONG Wenxiao, AN Xingwei, DI Yang, ZHANG Lixin, MING Dong. Review on identity feature extraction methods based on electroencephalogram signals. Journal of Biomedical Engineering, 2021, 38(6): 1203-1210. doi: 10.7507/1001-5515.202102057 Copy

1.	Gui Q, Ruiz-Blondet M V, Laszlo S, et al. A survey on brain biometrics. ACM Computing Surveys(CSUR), 2019, 51(6): 1-38.
2.	王英迪. 基于脑电信号的身份识别系统研究. 西安: 西安电子科技大学, 2019.
3.	Pani S M, Ciuffi M, Demuru M, et al. Subject, session and task effects on power, connectivity and network centrality: a source-based EEG study. Biomedical Signal Processing and Control, 2020, 59: 101891.
4.	Chan H L, Kuo P C, Cheng C Y, et al. Challenges and future perspectives on electroencephalogram-based biometrics in person recognition. Front Neuroinform, 2018, 12: 66.
5.	Moctezuma L A, Torres-García A A, Villaseñor-Pineda L, et al. Subjects identification using EEG-recorded imagined speech. Expert Systems with Applications, 2019, 118: 201-208.
6.	Sooriyaarachchi J, Seneviratne S, Thilakarathna K, et al. MusicID: a brainwave-based user authentication system for internet of things. IEEE Internet Things J, 2021, 8(10): 8304-8313.
7.	Paranjape R B, Mahovsky J, Benedicenti L, et al. The electroencephalogram as a biometric//Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No. 01TH8555), IEEE, 2001, 2: 1363-1366.
8.	Lin Feng, Cho K W, Song Chen, et al. Exploring a brain-based cancelable biometrics for smart headwear: concept, implementation, and evaluation. IEEE Trans Mob Comput, 2020, 19(12): 2774-2792.
9.	Maiorana E, Rocca D L, Campisi P. On the permanence of EEG signals for biometric recognition. IEEE Transactions on Information Forensics and Security, 2016, 11(1): 163-175.
10.	Maiorana E, Campisi P. Longitudinal evaluation of EEG-based biometric recognition. IEEE Transactions on Information Forensics and Security, 2018, 13(5): 1123-1138.
11.	Rocca D L, Campisi P, Vegso B, et al. Human brain distinctiveness based on EEG spectral coherence connectivity. IEEE Trans Biomed Eng, 2014, 61(9): 2406-2412.
12.	Di Yang, An Xingwei, He Feng, et al. Robustness analysis of identification using resting-state EEG signals. IEEE Access, 2019, 7: 42113-42122.
13.	Delpozo-Banos M, Travieso C M, Alonso J B, et al. Evidence of a task-independent neural signature in the spectral shape of the electroencephalogram. Int J Neural Syst, 2018, 28(1): 1750035.
14.	Yang S, Hoque S, Deravi F. Improved time-frequency features and electrode placement for EEG-based biometric person recognition. IEEE Access, 2019, 7: 49604-49613.
15.	Piciucco E, Maiorana E, Falzon O, et al. Steady-state visual evoked potentials for EEG-based biometric identification//2017 International Conference of the Biometrics Special Interest Group (BIOSIG), IEEE, 2017: 1-5.
16.	Waili T, Johar M G M, Sidek K A, et al. EEG based biometric identification using correlation and MLPNN models. International Journal of Online and Biomedical Engineering, 2019, 15(10): 77-90.
17.	Carrión-Ojeda D, Fonseca-Delgado R, Pineda I. Analysis of factors that influence the performance of biometric systems based on EEG signals. Expert Systems with Applications, 2021, 165: 113967.
18.	Yang B, Li H, Wang Q, et al. Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces. Comput Methods Programs Biomed, 2016, 129: 21-28.
19.	Phung D Q, Tran D, Ma W, et al. Using Shannon entropy as EEG signal feature for fast person identification// European Symposium on Artificial Neural Networks(ESANN). Bruges:Belgium, 2014, 4(1): 413-418.
20.	Chang W, Wang H, Yan G, et al. An EEG based familiar and unfamiliar person identification and classification system using feature extraction and directed functional brain network. Expert Systems with Applications, 2020, 158: 113448.
21.	Kang J H, Jo Y C, Kim S P. Electroencephalographic feature evaluation for improving personal authentication performance. Neurocomputing, 2018, 287: 93-101.
22.	Li Sukun, Cha S H. Feature extraction based on high order statistics measures and entropy for EEG biometrics//2019 7th International Workshop on Biometrics and Forensics (IWBF), IEEE, 2019: 1-6.
23.	胡理, 张治国. 脑电信号处理与特征提取. 北京:科学出版社, 2020: 239-265.
24.	蒋勤, 张毅, 刘鹏飞. 基于相位同步的运动想象分类. 华中科技大学学报:自然科学版, 2020, 48(1): 48-54.
25.	Cox R, Schapiro A C, Stickgold R. Variability and stability of large-scale cortical oscillation patterns. Netw Neurosci, 2018, 2(4): 481-512.
26.	Kong Wanzeng, Wang Luyun, Xu Sijia, et al. EEG fingerprints: phase synchronization of EEG signals as biomarker for subject identification. IEEE Access, 2019, 7: 121165-121173.
27.	Wang Min, El-Fiqi H, Hu Jiankun, et al. Convolutional neural networks using dynamic functional connectivity for EEG-based person identification in diverse human states. IEEE Transactions on Information Forensics and Security, 2019, 14(12): 3259-3272.
28.	Wang Min, Hu Jiankun, Abbass H A. BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs. Pattern Recognition, 2020, 105: 107381.
29.	Valizadeh S A, Riener R, Elmer S, et al. Decrypting the electrophysiological individuality of the human brain: identification of individuals based on resting-state EEG activity. Neuroimage, 2019, 197: 470-481.
30.	Min B K, Suk H I, Ahn M H, et al. Individual identification using cognitive electroencephalographic neurodynamics. IEEE Transactions on Information Forensics and Security, 2017, 12(9): 2159-2167.
31.	Duan W, Chen X, Wang Y J, et al. Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG. J Neurosci Methods, 2021, 348(8): 108985.
32.	Zhang Xiang, Yao Lina, Wang Xianzhi, et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. Journal of Neural Engineering, 2021, 18: 031002.
33.	Maiorana E. Deep learning for EEG-based biometric recognition. Neurocomputing, 2020, 410: 374-386.
34.	Mota M R F, Silva P H L, Luz E J S, A deep descriptor for cross-tasking EEG-based recognition. PeerJ Computer Science, 2021. DOI: 10.7717/peerj-cs.549.
35.	Yu Ting, Wei Chunshu, Chiang K J, et al. EEG-based user authentication using a convolutional neural network//2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, 2019: 1011-1014.
36.	Das B B, Kumar P, Kar D, et al. A spatio-temporal model for EEG-based person identification. Multimed Tools Appl, 2019, 78(19): 28157-28177.
37.	Sun Yingnan, Lo F P W, Lo B. EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert Systems with Applications, 2019, 125: 259-267.
38.	Wang Min, Abdelfattah S, Moustafa N, et al. Deep Gaussian mixture-hidden Markov model for classification of EEG signals. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(4): 278-287.
39.	Lai C Q, Ibrahim H, Abdullah M Z, et al. Arrangements of resting state electroencephalography as the input to convolutional neural network for biometric identification. Comput Intell Neurosci, 2019: 7895924.
40.	Özdenizci O, Wang Y, Koike-Akino T, et al. Adversarial deep learning in EEG biometrics. IEEE Signal Process Lett, 2019, 26(5): 710-714.
41.	Maiorana E. Learning deep features for task-independent EEG-based biometric verification. Pattern Recognition Letters, 2021, 143: 122-129.
42.	王韬, 柯余峰, 王宁慈, 等. 空间滤波方法在脑-机接口中的应用及研究进展. 中国生物医学工程学报, 2019, 38(5): 599-608.
43.	周晓宇, 许敏鹏, 肖晓琳, 等. 脑-机接口中脑电解码算法研究综述. 生物医学工程学杂志, 2019, 36(5): 856-861.
44.	Maiorana E, La Rocca D, Campisi P. Eigenbrains and eigentensorbrains: parsimonious bases for EEG biometrics. Neurocomputing, 2016, 171: 638-648.
45.	Zhao Hongze, Wang Yijun, Liu Zhiduo, et al. Individual identification based on code-modulated visual-evoked potentials. IEEE Transactions on Information Forensics and Security, 2019, 14(12): 3206-3216.
46.	Zhao H, Chen Y, Pei W, et al. Towards online applications of EEG biometrics using visual evoked potentials. Expert Systems with Applications, 2021: 114961.
47.	Nishimoto T, Higashi H, Morioka H, et al. EEG-based personal identification method using unsupervised feature extraction and its robustness against intra-subject variability. J Neural Eng, 2020, 17(2): 026007.
48.	Wang Min, Kasmarik K, Bezerianos A, et al. On the channel density of EEG signals for reliable biometric recognition. Pattern Recognition Letters, 2021, 147: 134-141.
49.	Kumar M G, Saranya M S, Narayanan S, et al. Subspace techniques for task-independent EEG person identification//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019: 4545-4548.

1. Gui Q, Ruiz-Blondet M V, Laszlo S, et al. A survey on brain biometrics. ACM Computing Surveys(CSUR), 2019, 51(6): 1-38.
2. 王英迪. 基于脑电信号的身份识别系统研究. 西安: 西安电子科技大学, 2019.
3. Pani S M, Ciuffi M, Demuru M, et al. Subject, session and task effects on power, connectivity and network centrality: a source-based EEG study. Biomedical Signal Processing and Control, 2020, 59: 101891.
4. Chan H L, Kuo P C, Cheng C Y, et al. Challenges and future perspectives on electroencephalogram-based biometrics in person recognition. Front Neuroinform, 2018, 12: 66.
5. Moctezuma L A, Torres-García A A, Villaseñor-Pineda L, et al. Subjects identification using EEG-recorded imagined speech. Expert Systems with Applications, 2019, 118: 201-208.
6. Sooriyaarachchi J, Seneviratne S, Thilakarathna K, et al. MusicID: a brainwave-based user authentication system for internet of things. IEEE Internet Things J, 2021, 8(10): 8304-8313.
7. Paranjape R B, Mahovsky J, Benedicenti L, et al. The electroencephalogram as a biometric//Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No. 01TH8555), IEEE, 2001, 2: 1363-1366.
8. Lin Feng, Cho K W, Song Chen, et al. Exploring a brain-based cancelable biometrics for smart headwear: concept, implementation, and evaluation. IEEE Trans Mob Comput, 2020, 19(12): 2774-2792.
9. Maiorana E, Rocca D L, Campisi P. On the permanence of EEG signals for biometric recognition. IEEE Transactions on Information Forensics and Security, 2016, 11(1): 163-175.
10. Maiorana E, Campisi P. Longitudinal evaluation of EEG-based biometric recognition. IEEE Transactions on Information Forensics and Security, 2018, 13(5): 1123-1138.
11. Rocca D L, Campisi P, Vegso B, et al. Human brain distinctiveness based on EEG spectral coherence connectivity. IEEE Trans Biomed Eng, 2014, 61(9): 2406-2412.
12. Di Yang, An Xingwei, He Feng, et al. Robustness analysis of identification using resting-state EEG signals. IEEE Access, 2019, 7: 42113-42122.
13. Delpozo-Banos M, Travieso C M, Alonso J B, et al. Evidence of a task-independent neural signature in the spectral shape of the electroencephalogram. Int J Neural Syst, 2018, 28(1): 1750035.
14. Yang S, Hoque S, Deravi F. Improved time-frequency features and electrode placement for EEG-based biometric person recognition. IEEE Access, 2019, 7: 49604-49613.
15. Piciucco E, Maiorana E, Falzon O, et al. Steady-state visual evoked potentials for EEG-based biometric identification//2017 International Conference of the Biometrics Special Interest Group (BIOSIG), IEEE, 2017: 1-5.
16. Waili T, Johar M G M, Sidek K A, et al. EEG based biometric identification using correlation and MLPNN models. International Journal of Online and Biomedical Engineering, 2019, 15(10): 77-90.
17. Carrión-Ojeda D, Fonseca-Delgado R, Pineda I. Analysis of factors that influence the performance of biometric systems based on EEG signals. Expert Systems with Applications, 2021, 165: 113967.
18. Yang B, Li H, Wang Q, et al. Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces. Comput Methods Programs Biomed, 2016, 129: 21-28.
19. Phung D Q, Tran D, Ma W, et al. Using Shannon entropy as EEG signal feature for fast person identification// European Symposium on Artificial Neural Networks(ESANN). Bruges:Belgium, 2014, 4(1): 413-418.
20. Chang W, Wang H, Yan G, et al. An EEG based familiar and unfamiliar person identification and classification system using feature extraction and directed functional brain network. Expert Systems with Applications, 2020, 158: 113448.
21. Kang J H, Jo Y C, Kim S P. Electroencephalographic feature evaluation for improving personal authentication performance. Neurocomputing, 2018, 287: 93-101.
22. Li Sukun, Cha S H. Feature extraction based on high order statistics measures and entropy for EEG biometrics//2019 7th International Workshop on Biometrics and Forensics (IWBF), IEEE, 2019: 1-6.
23. 胡理, 张治国. 脑电信号处理与特征提取. 北京:科学出版社, 2020: 239-265.
24. 蒋勤, 张毅, 刘鹏飞. 基于相位同步的运动想象分类. 华中科技大学学报:自然科学版, 2020, 48(1): 48-54.
25. Cox R, Schapiro A C, Stickgold R. Variability and stability of large-scale cortical oscillation patterns. Netw Neurosci, 2018, 2(4): 481-512.
26. Kong Wanzeng, Wang Luyun, Xu Sijia, et al. EEG fingerprints: phase synchronization of EEG signals as biomarker for subject identification. IEEE Access, 2019, 7: 121165-121173.
27. Wang Min, El-Fiqi H, Hu Jiankun, et al. Convolutional neural networks using dynamic functional connectivity for EEG-based person identification in diverse human states. IEEE Transactions on Information Forensics and Security, 2019, 14(12): 3259-3272.
28. Wang Min, Hu Jiankun, Abbass H A. BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs. Pattern Recognition, 2020, 105: 107381.
29. Valizadeh S A, Riener R, Elmer S, et al. Decrypting the electrophysiological individuality of the human brain: identification of individuals based on resting-state EEG activity. Neuroimage, 2019, 197: 470-481.
30. Min B K, Suk H I, Ahn M H, et al. Individual identification using cognitive electroencephalographic neurodynamics. IEEE Transactions on Information Forensics and Security, 2017, 12(9): 2159-2167.
31. Duan W, Chen X, Wang Y J, et al. Reproducibility of power spectrum, functional connectivity and network construction in resting-state EEG. J Neurosci Methods, 2021, 348(8): 108985.
32. Zhang Xiang, Yao Lina, Wang Xianzhi, et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. Journal of Neural Engineering, 2021, 18: 031002.
33. Maiorana E. Deep learning for EEG-based biometric recognition. Neurocomputing, 2020, 410: 374-386.
34. Mota M R F, Silva P H L, Luz E J S, A deep descriptor for cross-tasking EEG-based recognition. PeerJ Computer Science, 2021. DOI: 10.7717/peerj-cs.549.
35. Yu Ting, Wei Chunshu, Chiang K J, et al. EEG-based user authentication using a convolutional neural network//2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE, 2019: 1011-1014.
36. Das B B, Kumar P, Kar D, et al. A spatio-temporal model for EEG-based person identification. Multimed Tools Appl, 2019, 78(19): 28157-28177.
37. Sun Yingnan, Lo F P W, Lo B. EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert Systems with Applications, 2019, 125: 259-267.
38. Wang Min, Abdelfattah S, Moustafa N, et al. Deep Gaussian mixture-hidden Markov model for classification of EEG signals. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, 2(4): 278-287.
39. Lai C Q, Ibrahim H, Abdullah M Z, et al. Arrangements of resting state electroencephalography as the input to convolutional neural network for biometric identification. Comput Intell Neurosci, 2019: 7895924.
40. Özdenizci O, Wang Y, Koike-Akino T, et al. Adversarial deep learning in EEG biometrics. IEEE Signal Process Lett, 2019, 26(5): 710-714.
41. Maiorana E. Learning deep features for task-independent EEG-based biometric verification. Pattern Recognition Letters, 2021, 143: 122-129.
42. 王韬, 柯余峰, 王宁慈, 等. 空间滤波方法在脑-机接口中的应用及研究进展. 中国生物医学工程学报, 2019, 38(5): 599-608.
43. 周晓宇, 许敏鹏, 肖晓琳, 等. 脑-机接口中脑电解码算法研究综述. 生物医学工程学杂志, 2019, 36(5): 856-861.
44. Maiorana E, La Rocca D, Campisi P. Eigenbrains and eigentensorbrains: parsimonious bases for EEG biometrics. Neurocomputing, 2016, 171: 638-648.
45. Zhao Hongze, Wang Yijun, Liu Zhiduo, et al. Individual identification based on code-modulated visual-evoked potentials. IEEE Transactions on Information Forensics and Security, 2019, 14(12): 3206-3216.
46. Zhao H, Chen Y, Pei W, et al. Towards online applications of EEG biometrics using visual evoked potentials. Expert Systems with Applications, 2021: 114961.
47. Nishimoto T, Higashi H, Morioka H, et al. EEG-based personal identification method using unsupervised feature extraction and its robustness against intra-subject variability. J Neural Eng, 2020, 17(2): 026007.
48. Wang Min, Kasmarik K, Bezerianos A, et al. On the channel density of EEG signals for reliable biometric recognition. Pattern Recognition Letters, 2021, 147: 134-141.
49. Kumar M G, Saranya M S, Narayanan S, et al. Subspace techniques for task-independent EEG person identification//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019: 4545-4548.

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