A review of researches on electroencephalogram decoding algorithms in brain-computer interface_Journal of Biomedical Engineering

Authors：

ZHOU Xiaoyu ¹ ,  XU Minpeng ^1,2 , XIAO Xiaolin ¹ , CHEN Long ² , GU Xiaosong ^1,2 , MING Dong ^1,2

1. School of Precision Instrument and Opto-electronics Engineering, TianJin University, TianJin 300072, P.R.China;
2. Academy of Medical Engineering and Translational Medicine. TianJin University, TianJin 300072, P.R.China;

Corresponding?author：

XU Minpeng, Email: minpeng.xu@tju.edu.cn

Keywords：

brain-computer interface; electroencephalogram; feature extraction; pattern recognition

DOI：

10.7507/1001-5515.201812049

Video：

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Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.

Citation： ZHOU Xiaoyu, XU Minpeng, XIAO Xiaolin, CHEN Long, GU Xiaosong, MING Dong. A review of researches on electroencephalogram decoding algorithms in brain-computer interface. Journal of Biomedical Engineering, 2019, 36(5): 856-861. doi: 10.7507/1001-5515.201812049 Copy

1.	Ramadan R A, Vasilakos A V. Brain computer interface: control signals review. Neurocomputing, 2017, 223: 26-44.
2.	Chaudhary U, Xia Bin, Silvoni S, et al. Brain-computer interface-based communication in the completely locked-in state. PLoS Biol, 2017, 15(1): e1002593.
3.	Chowdhury A, Shankaran R, Kavakli M, et al. Sensor applications and physiological features in drivers' drowsiness detection: a review. IEEE Sens J, 2018, 18(8): 3055-3067.
4.	Munyon C N. Neuroethics of non-primary brain computer interface: focus on potential military applications. Front Neurosci, 2018, 12: 696.
5.	Ge Sheng, Ding Mengyuan, Zhang Zheng, et al. Temporal-spatial features of intention understanding based on EEG-fNIRS bimodal measurement. IEEE Access, 2017, 5: 14245-14258.
6.	Zhang Li, Gan J Q, Wang Haixian. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method. Cogn Neurodyn, 2015, 9(5): 495-508.
7.	Lotte F, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng, 2018, 15(3): 031005.
8.	Wu Chaohua, Lin Ke, Wu Wei, et al. A novel algorithm for learning sparse spatio-spectral patterns for event-related potentials. IEEE Trans Neural Netw Learn Syst, 2017, 28(4): 862-872.
9.	Zhang Yu, Zhou Guoxu, Jin Jing, et al. L1-Regularized multiway canonical correlation analysis for SSVEP-based BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(6): 887-896.
10.	Nakanishi M, Wang Yijun, Wang Yute, et al. A high-speed brain speller using steady-state visual evoked potentials. Int J Neural Syst, 2014, 24(6): 1450019.
11.	Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12(4): 046008.
12.	Wang Haixian, Li Xiaomeng. Regularized filters for L1-Norm-Based common spatial patterns. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(2): 201-211.
13.	Hatamikia S, Nasrabadi A M. Subject transfer BCI based on composite local temporal correlation common spatial pattern. Comput Biol Med, 2015, 64: 1-11.
14.	Lotte F, Guan Cuntai. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng, 2011, 58(2): 355-362.
15.	Tahernezhad-Javazm F, Azimirad V, Shoaran M. A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems. J Neural Eng, 2018, 15(2): 021007.
16.	Quitadamo L R, Cavrini F, Sbernini L, et al. Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review. J Neural Eng, 2017, 14(1): 011001.
17.	Zhang Yu, Zhou Guoxu, Jin Jing, et al. Sparse bayesian classification of EEG for brain-computer interface. IEEE Trans Neural Netw Learn Syst, 2016, 27(11): 2256-2267.
18.	Jiang Jing, Yin Erwei, Wang Chunhui, et al. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J Neural Eng, 2018, 15(4): 046025.
19.	Idaji M J, Shamsollahi M B, Sardouie S H. Higher order spectral regression discriminant analysis (HOSRDA): a tensor feature reduction method for ERP detection. Pattern Recognit, 2017, 70: 152-162.
20.	Nakanishi M, Wang Yijun, Chen Xiaogang, et al. Enhancing detection of SSVEPs for a High-Speed brain speller using task-related component analysis. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
21.	Xu Minpeng, Xiao Xiaolin, Wang Yijun, et al. A brain-computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng, 2018, 65(5): 1166-1175.
22.	Ilea I, Bombrun L, Said S, et al. Fisher vector coding for covariance matrix descriptors based on the log-euclidean and affine invariant riemannian metrics. Journal Of Imaging, 2018, 4(7): 85.
23.	Congedo M, Barachant A, Bhatia R. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Computer Interfaces, 2017, 4(3): 155-174.
24.	Yger F, Berar M, Lotte F. Riemannian approaches in brain-computer interfaces: a review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(10): 1753-1762.
25.	Waytowich N R, Lawhern V J, Bohannon A W, et al. Spectral transfer learning using information geometry for a user-independent brain-computer interface. Front Neurosci, 2016, 10: 430.
26.	Nguyen C H, Artemiadis P. EEG feature descriptors and discriminant analysis under riemannian manifold perspective. Neurocomputing, 2018, 275: 1871-1883.
27.	Kalunga E K, Chevallier S, Barthelemy Q A, et al. Online SSVEP-based BCI using riemannian geometry. Neurocomputing, 2016, 191: 55-68.
28.	Cao Chensi, Liu Feng, Tan Hai, et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics, 2018, 16(1): 17-32.
29.	Movahedi F, Coyle J L, Sejdic E. Deep belief networks for electroencephalography: a review of recent contributions and future outlooks. IEEE J Biomed Health Inform, 2018, 22(3): 642-652.
30.	Schirrmeister R T, Springenberg J T, Fiederer L D, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp, 2017, 38(11): 5391-5420.
31.	Tabar Y R, Halici U. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng, 2017, 14(1): 016003.
32.	Ma Teng, Li Hui, Yang Hao, et al. The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing. J Neurosci Methods, 2017, 275: 80-92.

1. Ramadan R A, Vasilakos A V. Brain computer interface: control signals review. Neurocomputing, 2017, 223: 26-44.
2. Chaudhary U, Xia Bin, Silvoni S, et al. Brain-computer interface-based communication in the completely locked-in state. PLoS Biol, 2017, 15(1): e1002593.
3. Chowdhury A, Shankaran R, Kavakli M, et al. Sensor applications and physiological features in drivers' drowsiness detection: a review. IEEE Sens J, 2018, 18(8): 3055-3067.
4. Munyon C N. Neuroethics of non-primary brain computer interface: focus on potential military applications. Front Neurosci, 2018, 12: 696.
5. Ge Sheng, Ding Mengyuan, Zhang Zheng, et al. Temporal-spatial features of intention understanding based on EEG-fNIRS bimodal measurement. IEEE Access, 2017, 5: 14245-14258.
6. Zhang Li, Gan J Q, Wang Haixian. Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method. Cogn Neurodyn, 2015, 9(5): 495-508.
7. Lotte F, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng, 2018, 15(3): 031005.
8. Wu Chaohua, Lin Ke, Wu Wei, et al. A novel algorithm for learning sparse spatio-spectral patterns for event-related potentials. IEEE Trans Neural Netw Learn Syst, 2017, 28(4): 862-872.
9. Zhang Yu, Zhou Guoxu, Jin Jing, et al. L1-Regularized multiway canonical correlation analysis for SSVEP-based BCI. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(6): 887-896.
10. Nakanishi M, Wang Yijun, Wang Yute, et al. A high-speed brain speller using steady-state visual evoked potentials. Int J Neural Syst, 2014, 24(6): 1450019.
11. Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12(4): 046008.
12. Wang Haixian, Li Xiaomeng. Regularized filters for L1-Norm-Based common spatial patterns. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(2): 201-211.
13. Hatamikia S, Nasrabadi A M. Subject transfer BCI based on composite local temporal correlation common spatial pattern. Comput Biol Med, 2015, 64: 1-11.
14. Lotte F, Guan Cuntai. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng, 2011, 58(2): 355-362.
15. Tahernezhad-Javazm F, Azimirad V, Shoaran M. A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems. J Neural Eng, 2018, 15(2): 021007.
16. Quitadamo L R, Cavrini F, Sbernini L, et al. Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review. J Neural Eng, 2017, 14(1): 011001.
17. Zhang Yu, Zhou Guoxu, Jin Jing, et al. Sparse bayesian classification of EEG for brain-computer interface. IEEE Trans Neural Netw Learn Syst, 2016, 27(11): 2256-2267.
18. Jiang Jing, Yin Erwei, Wang Chunhui, et al. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J Neural Eng, 2018, 15(4): 046025.
19. Idaji M J, Shamsollahi M B, Sardouie S H. Higher order spectral regression discriminant analysis (HOSRDA): a tensor feature reduction method for ERP detection. Pattern Recognit, 2017, 70: 152-162.
20. Nakanishi M, Wang Yijun, Chen Xiaogang, et al. Enhancing detection of SSVEPs for a High-Speed brain speller using task-related component analysis. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
21. Xu Minpeng, Xiao Xiaolin, Wang Yijun, et al. A brain-computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng, 2018, 65(5): 1166-1175.
22. Ilea I, Bombrun L, Said S, et al. Fisher vector coding for covariance matrix descriptors based on the log-euclidean and affine invariant riemannian metrics. Journal Of Imaging, 2018, 4(7): 85.
23. Congedo M, Barachant A, Bhatia R. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Computer Interfaces, 2017, 4(3): 155-174.
24. Yger F, Berar M, Lotte F. Riemannian approaches in brain-computer interfaces: a review. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(10): 1753-1762.
25. Waytowich N R, Lawhern V J, Bohannon A W, et al. Spectral transfer learning using information geometry for a user-independent brain-computer interface. Front Neurosci, 2016, 10: 430.
26. Nguyen C H, Artemiadis P. EEG feature descriptors and discriminant analysis under riemannian manifold perspective. Neurocomputing, 2018, 275: 1871-1883.
27. Kalunga E K, Chevallier S, Barthelemy Q A, et al. Online SSVEP-based BCI using riemannian geometry. Neurocomputing, 2016, 191: 55-68.
28. Cao Chensi, Liu Feng, Tan Hai, et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics, 2018, 16(1): 17-32.
29. Movahedi F, Coyle J L, Sejdic E. Deep belief networks for electroencephalography: a review of recent contributions and future outlooks. IEEE J Biomed Health Inform, 2018, 22(3): 642-652.
30. Schirrmeister R T, Springenberg J T, Fiederer L D, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp, 2017, 38(11): 5391-5420.
31. Tabar Y R, Halici U. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng, 2017, 14(1): 016003.
32. Ma Teng, Li Hui, Yang Hao, et al. The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing. J Neurosci Methods, 2017, 275: 80-92.

Journal of Biomedical Engineering

A review of researches on electroencephalogram decoding algorithms in brain-computer interface

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