Progress of classification algorithms for motor imagery electroencephalogram signals_Journal of Biomedical Engineering

Authors：

LIU Tuo ^1,3 , YE Yangyang ^2,3 , WANG Kun ^2,3 , XU Lichao ^2,3 , YI Weibo ⁴ ,  XU Minpeng ^1,2,3 , MING Dong ^1,2,3

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;
3. Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin 300072, P.R.China;
4. Beijing Machine and Equipment Institute, Beijing 100854, P.R.China;

Corresponding author：

Keywords：

motor imagery electroencephalogram; classifiers; machine learning strategies

DOI：

10.7507/1001-5515.202101089

Video：

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Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.

Citation： LIU Tuo, YE Yangyang, WANG Kun, XU Lichao, YI Weibo, XU Minpeng, MING Dong. Progress of classification algorithms for motor imagery electroencephalogram signals. Journal of Biomedical Engineering, 2021, 38(5): 995-1002. doi: 10.7507/1001-5515.202101089 Copy

1.	Hetu S, Gregoire M, Saimpont A, et al. The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav R, 2013, 37(5): 930-949.
2.	Torres P E, Torres E A, Hernandez Alvarez M, et al. EEG-based BCI emotion recognition: a survey. Sensors(Basel), 2020, 20(18): 5083.
3.	Liu Yiliang, Su Wenbin, Li Zhijun, et al. Motor-imagery-based teleoperation of a dual-arm robot performing manipulation tasks. IEEE Trans Cogn Dev Syst, 2019, 11(3): 414-424.
4.	Nourmohammadi A, Jafari M, Zander T O. A survey on unmanned aerial vehicle remote control using brain–computer interface. IEEE Trans Hum-Mach Syst, 2018, 48(4): 337-348.
5.	Wang H, Li T, Bezerianos A, et al. The control of a virtual automatic car based on multiple patterns of motor imagery BCI. Med Biol Eng Comput, 2019, 57(1): 299-309.
6.	Tariq M, Trivailo P M, Simic M. Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI. PLoS One, 2020, 15(3): e0230184.
7.	Wang Kun, Xu Minpeng, Wang Yijun, et al. Enhance decoding of pre-movement EEG patterns for brain-computer interfaces. J Neural Eng, 2020, 17(1): 016033.
8.	Lotte F, Congedo M, Lecuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng, 2007, 4(2): R1-R13.
9.	Kevric J, Subasi A. Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Proces, 2017, 31: 398-406.
10.	周志华. 机器学习. 北京: 清华大学出版社, 2016: 53.
11.	Lotte F. Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain–computer interfaces. Pro IEEE, 2015, 103(6): 871-890.
12.	Razzak I, Hameed I A, Xu G. Robust sparse representation and multiclass support matrix machines for the classification of motor imagery EEG signals. IEEE J Transl Eng He, 2019, 7: 1-8.
13.	Saleh A I, Shehata S A, Labeeb L M. A fuzzy-based classification strategy (FBCS) based on brain-computer interface. Soft Comput, 2019, 23(7): 2343-2367.
14.	Miao Minmin, Zeng Hong, Wang Aimin, et al. Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: an sparse regression and weighted naive bayesian classifier-based approach. J Neurosci Methods, 2017, 278: 13-24.
15.	Craik A, He Y, Contreras Vidal J L. Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng, 2019, 16(3): 031001.
16.	Olivas-Padilla B E, Chacon-Murguia M I. Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl Soft Comput, 2019, 75: 461-472.
17.	Sakhavi S, Guan C, Yan S. Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans Neural Netw Learn Syst, 2018, 29(11): 5619-5629.
18.	Xu Baoguo, Zhang Linlin, Song Aiguo, et al. Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. IEEE Access, 2019, 7: 6084-6093.
19.	Lun Xiangmin, Yu Zhenglin, Chen Tao, et al. A simplified CNN classification method for MI-EEG via the electrode pairs signals. Front Hum Neurosci, 2020, 14: 338.
20.	Li G, Lee C H, Jung J J, et al. Deep learning for EEG data analytics: a survey. Concurr Comp-Pract E, 2019, 32(18): e5199.
21.	Luo T-J, Zhou C-L, Chao F. Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. Bmc Bioinformatics, 2018, 19: 344.
22.	Xu Jiacan, Zheng Hao, Wang Jianhui, et al. Recognition of EEG signal motor imagery intention based on deep multi-view feature learning. Sensors(Basel), 2020, 20(12): 3496.
23.	Lu Na, Li Tengfei, Ren Xiaodong, et al. A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(6): 566-576.
24.	Yang Jun, Yao Shaowen, Wang Jin. Deep fusion feature learning network for MI-EEG classification. IEEE Access, 2018, 6: 79050-79059.
25.	Ha K W, Jeong J W. Motor imagery EEG classification using capsule networks. Sensors(Basel), 2019, 19(13): 2854.
26.	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.
27.	Yger F, Berar M, Lotte F. Riemannian approaches in brain-computer interfaces: a review. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(10): 1753-1762.
28.	Barachant A, Bonnet S, Congedo M, et al. Multiclass brain-computer interface classification by Riemannian geometry. IEEE Trans Biomed Eng, 2012, 59(4): 920-928.
29.	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.
30.	Singh A, Lal S, Guesgen H W. Small sample motor imagery classification using regularized riemannian features. IEEE Access, 2019, 7: 46858-46869.
31.	Tang F, Fan M, Tino P. Generalized learning riemannian space quantization: a case study on riemannian manifold of SPD matrices. IEEE Trans Neural Netw Learn Syst, 2020, 32(1): 281-292.
32.	Xie Xiaofeng, Yu Zhu Liang, Gu Zhenghui, et al. Bilinear regularized locality preserving learning on riemannian graph for motor imagery BCI. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(3): 698-708.
33.	Shin J. Random subspace ensemble learning for functional near-infrared spectroscopy brain-computer interfaces. Front Hum Neurosci, 2020, 14: 236.
34.	Zhang Ranran, Xiao Xiaoyan, Liu Zhi, et al. A new motor imagery EEG classification method FB-TRCSP+RF based on CSP and random forest. IEEE Access, 2018, 6: 44944-44950.
35.	Luo Jing, Gao Xing, Zhu Xiaobei, et al. Motor imagery EEG classification based on ensemble support vector learning. Comput Methods Programs Biomed, 2020, 193: 105464.
36.	Zhang Li, Wen Dezhong, Li Changsheng, et al. Ensemble classifier based on optimized extreme learning machine for motor imagery classification. J Neural Eng, 2020, 17(2): 026004.
37.	Talukdar U, Hazarika S M, Gan J Q. Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue. J Neural Eng, 2020, 17(1): 016020.
38.	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.
39.	Talukdar U, Hazarika S M, Gan J Q. Adaptation of common spatial patterns based on mental fatigue for motor-imagery BCI. Biomed Signal Proces, 2020, 58: 101829.
40.	Priyatharshini R, Chitrakala S. A self-learning fuzzy rule-based system for risk-level assessment of coronary heart disease. IETE J Res, 2018, 65(3): 288-297.
41.	Komijani H, Parsaei M R, Khajeh E, et al. EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction. Neural Comput Appl, 2019, 31(7): 2551-2562.
42.	Saha S, Baumert M. Intra- and inter-subject variability in EEG-based sensorimotor brain computer interface: a review. Front Comput Neurosci, 2019, 13: 87.
43.	Pan S J, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng, 2010, 22(10): 1345-1359.
44.	Azab A M, Mihaylova L, Ang K K, et al. Weighted transfer learning for improving motor imagery-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(7): 1352-1359.
45.	Wu Ha, Niu Yi, Li Fu, et al. A parallel multiscale filter bank convolutional neural networks for motor imagery EEG classification. Front Neurosci, 2019, 13: 1275.
46.	Tan Chuanqi, Sun Fuchun, Fang Bin, et al. Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot. Int J Adv Robot Syst, 2019, 16(2): 1729881419840860.
47.	Xu Lichao, Xu Minpeng, Ke Yufeng, et al. Cross-dataset variability problem in EEG decoding with deep learning. Front Hum Neurosci, 2020, 14: 103.

1. Hetu S, Gregoire M, Saimpont A, et al. The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav R, 2013, 37(5): 930-949.
2. Torres P E, Torres E A, Hernandez Alvarez M, et al. EEG-based BCI emotion recognition: a survey. Sensors(Basel), 2020, 20(18): 5083.
3. Liu Yiliang, Su Wenbin, Li Zhijun, et al. Motor-imagery-based teleoperation of a dual-arm robot performing manipulation tasks. IEEE Trans Cogn Dev Syst, 2019, 11(3): 414-424.
4. Nourmohammadi A, Jafari M, Zander T O. A survey on unmanned aerial vehicle remote control using brain–computer interface. IEEE Trans Hum-Mach Syst, 2018, 48(4): 337-348.
5. Wang H, Li T, Bezerianos A, et al. The control of a virtual automatic car based on multiple patterns of motor imagery BCI. Med Biol Eng Comput, 2019, 57(1): 299-309.
6. Tariq M, Trivailo P M, Simic M. Mu-Beta event-related (de)synchronization and EEG classification of left-right foot dorsiflexion kinaesthetic motor imagery for BCI. PLoS One, 2020, 15(3): e0230184.
7. Wang Kun, Xu Minpeng, Wang Yijun, et al. Enhance decoding of pre-movement EEG patterns for brain-computer interfaces. J Neural Eng, 2020, 17(1): 016033.
8. Lotte F, Congedo M, Lecuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng, 2007, 4(2): R1-R13.
9. Kevric J, Subasi A. Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Proces, 2017, 31: 398-406.
10. 周志华. 机器学习. 北京: 清华大学出版社, 2016: 53.
11. Lotte F. Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain–computer interfaces. Pro IEEE, 2015, 103(6): 871-890.
12. Razzak I, Hameed I A, Xu G. Robust sparse representation and multiclass support matrix machines for the classification of motor imagery EEG signals. IEEE J Transl Eng He, 2019, 7: 1-8.
13. Saleh A I, Shehata S A, Labeeb L M. A fuzzy-based classification strategy (FBCS) based on brain-computer interface. Soft Comput, 2019, 23(7): 2343-2367.
14. Miao Minmin, Zeng Hong, Wang Aimin, et al. Discriminative spatial-frequency-temporal feature extraction and classification of motor imagery EEG: an sparse regression and weighted naive bayesian classifier-based approach. J Neurosci Methods, 2017, 278: 13-24.
15. Craik A, He Y, Contreras Vidal J L. Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng, 2019, 16(3): 031001.
16. Olivas-Padilla B E, Chacon-Murguia M I. Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl Soft Comput, 2019, 75: 461-472.
17. Sakhavi S, Guan C, Yan S. Learning temporal information for brain-computer interface using convolutional neural networks. IEEE Trans Neural Netw Learn Syst, 2018, 29(11): 5619-5629.
18. Xu Baoguo, Zhang Linlin, Song Aiguo, et al. Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. IEEE Access, 2019, 7: 6084-6093.
19. Lun Xiangmin, Yu Zhenglin, Chen Tao, et al. A simplified CNN classification method for MI-EEG via the electrode pairs signals. Front Hum Neurosci, 2020, 14: 338.
20. Li G, Lee C H, Jung J J, et al. Deep learning for EEG data analytics: a survey. Concurr Comp-Pract E, 2019, 32(18): e5199.
21. Luo T-J, Zhou C-L, Chao F. Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network. Bmc Bioinformatics, 2018, 19: 344.
22. Xu Jiacan, Zheng Hao, Wang Jianhui, et al. Recognition of EEG signal motor imagery intention based on deep multi-view feature learning. Sensors(Basel), 2020, 20(12): 3496.
23. Lu Na, Li Tengfei, Ren Xiaodong, et al. A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(6): 566-576.
24. Yang Jun, Yao Shaowen, Wang Jin. Deep fusion feature learning network for MI-EEG classification. IEEE Access, 2018, 6: 79050-79059.
25. Ha K W, Jeong J W. Motor imagery EEG classification using capsule networks. Sensors(Basel), 2019, 19(13): 2854.
26. 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.
27. Yger F, Berar M, Lotte F. Riemannian approaches in brain-computer interfaces: a review. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(10): 1753-1762.
28. Barachant A, Bonnet S, Congedo M, et al. Multiclass brain-computer interface classification by Riemannian geometry. IEEE Trans Biomed Eng, 2012, 59(4): 920-928.
29. 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.
30. Singh A, Lal S, Guesgen H W. Small sample motor imagery classification using regularized riemannian features. IEEE Access, 2019, 7: 46858-46869.
31. Tang F, Fan M, Tino P. Generalized learning riemannian space quantization: a case study on riemannian manifold of SPD matrices. IEEE Trans Neural Netw Learn Syst, 2020, 32(1): 281-292.
32. Xie Xiaofeng, Yu Zhu Liang, Gu Zhenghui, et al. Bilinear regularized locality preserving learning on riemannian graph for motor imagery BCI. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(3): 698-708.
33. Shin J. Random subspace ensemble learning for functional near-infrared spectroscopy brain-computer interfaces. Front Hum Neurosci, 2020, 14: 236.
34. Zhang Ranran, Xiao Xiaoyan, Liu Zhi, et al. A new motor imagery EEG classification method FB-TRCSP+RF based on CSP and random forest. IEEE Access, 2018, 6: 44944-44950.
35. Luo Jing, Gao Xing, Zhu Xiaobei, et al. Motor imagery EEG classification based on ensemble support vector learning. Comput Methods Programs Biomed, 2020, 193: 105464.
36. Zhang Li, Wen Dezhong, Li Changsheng, et al. Ensemble classifier based on optimized extreme learning machine for motor imagery classification. J Neural Eng, 2020, 17(2): 026004.
37. Talukdar U, Hazarika S M, Gan J Q. Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue. J Neural Eng, 2020, 17(1): 016020.
38. 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.
39. Talukdar U, Hazarika S M, Gan J Q. Adaptation of common spatial patterns based on mental fatigue for motor-imagery BCI. Biomed Signal Proces, 2020, 58: 101829.
40. Priyatharshini R, Chitrakala S. A self-learning fuzzy rule-based system for risk-level assessment of coronary heart disease. IETE J Res, 2018, 65(3): 288-297.
41. Komijani H, Parsaei M R, Khajeh E, et al. EEG classification using recurrent adaptive neuro-fuzzy network based on time-series prediction. Neural Comput Appl, 2019, 31(7): 2551-2562.
42. Saha S, Baumert M. Intra- and inter-subject variability in EEG-based sensorimotor brain computer interface: a review. Front Comput Neurosci, 2019, 13: 87.
43. Pan S J, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng, 2010, 22(10): 1345-1359.
44. Azab A M, Mihaylova L, Ang K K, et al. Weighted transfer learning for improving motor imagery-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(7): 1352-1359.
45. Wu Ha, Niu Yi, Li Fu, et al. A parallel multiscale filter bank convolutional neural networks for motor imagery EEG classification. Front Neurosci, 2019, 13: 1275.
46. Tan Chuanqi, Sun Fuchun, Fang Bin, et al. Autoencoder-based transfer learning in brain–computer interface for rehabilitation robot. Int J Adv Robot Syst, 2019, 16(2): 1729881419840860.
47. Xu Lichao, Xu Minpeng, Ke Yufeng, et al. Cross-dataset variability problem in EEG decoding with deep learning. Front Hum Neurosci, 2020, 14: 103.

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