Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention_Journal of Biomedical Engineering

Authors：

 LI Hongli ¹ , YIN Feichao ¹ , ZHANG Ronghua ² , MA Xin ³ , CHEN Hongyu ¹

1. School of Control Science and Engineering, Tiangong University, Tianjin 300387, P. R. China;
2. School of Artificial Intelligence, Tiangong University, Tianjin 300387, P. R. China;
3. School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, P. R. China;

Corresponding author：

LI Hongli, Email: lihongliln@163.com

Keywords：

Brain-computer interface; Motor imagery; Attention mechanism; Sparse decomposition; Deep learning

DOI：

10.7507/1001-5515.202111031

Video：

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Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.

Citation： LI Hongli, YIN Feichao, ZHANG Ronghua, MA Xin, CHEN Hongyu. Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention. Journal of Biomedical Engineering, 2022, 39(3): 488-497. doi: 10.7507/1001-5515.202111031 Copy

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19.	Sun Biao, Zhao Xing, Zhang Han, et al. EEG motor imagery classification with sparse spectrotemporal decomposition and deep learning. IEEE T Autom Sci Eng, 2020, 18(22): 541-551.
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22.	Chaudhary S, Taran S, Bajaj V, et al. Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens J, 2019, 19(12): 4494-4500.
23.	Donoho D L. Compressed sensing. IEEE T Inform Theory, 2006, 52(4): 1289-1306.
24.	Schamberg G, Ba D, Coleman T P. A modularized efficient framework for non-Markov time series estimation. IEEE T Signal Proces, 2018, 66(12): 3140-3154.
25.	Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alter nating direction method of multipliers. Boston-Delft: Now Foundations and Trends, 2011.
26.	Hu Yaohua, Li Chong, Meng Kaiwen, et al. Group sparse optimization via l (p), (q) regularization. J Mach Learn Res, 2017, 18(1): 960-1011.
27.	Cao Yudong, Ding Yifei, Jia Minping, et al. A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. Reliab Eng Syst Saf, 2021, 215: 107813.
28.	Kingma D P, Ba J. Adam: a method for stochastic optimization// The 3rd International Conference on Learning Representations, San Diego, 2015. arXiv, 2015: 1412.6980.
29.	Schirrmeister R T, Springenberg J T, Fiederer L D J, et al. Deeplearning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp, 2017, 38(11): 5391-5420.
30.	Amin S U, Alsulaiman M, Muhammad G, et al. Deep learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Gener Comput Syst, 2019, 101: 542-554.
31.	Lawhern V J, Solon A J, Waytowich N R, et al. EEGNet: a compact convolutional neural network for EEG-based braincomputer interfaces. J Neural Eng, 2018, 15(5): 056013.1-056013.17.
32.	何群, 杜硕, 张园园, 等. 融合单通道框架及多通道框架的运动想象分类. 仪器仪表学报, 2018, 39(9): 20-29.
33.	张绍荣, 赵紫宁, 莫云, 等. 特征提取对通道选择方法的影响研究. 国外电子测量技术, 2020, 39(9): 1-6.

1. Ball T, Demandt E, Mutschler I, et al. Movement related activity in the high gamma range of the human EEG. Neuroimage, 2008, 41(2): 302-310.
2. Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-computer interface technology: A review of the first international meeting. IEEE Trans Rehabil Eng, 2000, 8(2): 164-173.
3. Chaudhary U, Birbaumer N, Ramos M A. Brain–computer interfaces for communication and rehabilitation. Nat Rev Neurol, 2016, 12(9): 513.
4. Lotte F, Bougrain L, Cichocki A, et al. A review of classifification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng, 2018, 15(3): 031005.
5. Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery: BCI and motor imagery. Ann Neurol, 2015, 77(5): 851-865.
6. 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.
7. Raza H, Chowdhury A, Bhattacharyya S, et al. Single-trial EEG classification with EEGNet and neural structured learning for improving BCI performance// IEEE International Joint Conference on Neural Networks (IJCNN). Glasgow: IEEE, 2020: 1-8.
8. Thomas K P, Guan C, Lau C T, et al. A new discriminative common spatial pattern method for motor imagery brain–computer interfaces. IEEE T Bio-Med Eng, 2009, 56(11): 2730-2733.
9. 王玉潇, 姜威, 刘治, 等. 基于共空间模式算法和支持向量机二重分类的癫痫发病预测. 生物医学工程学杂志, 2021, 38(1): 39-46.
10. 孟明, 尹旭, 高云园, 等. 运动想象脑电的块选择共空间模式特征提取. 控制理论与应用, 2021, 38(3): 301-308.
11. Ang K K, Chin Z Y, Zhang H, et al. Filter bank common spatial pattern (FBCSP) in brain-computer interface// IEEE International Joint Conference on Neural Networks (IJSCNN). Hong Kong: IEEE, 2008: 2390-2397.
12. Zayyanu S, Li Q. Optimized DNN classification framework based on filter bank common spatial pattern (FBCSP) for motor- imagery-based BCI. Int J Comput Appl, 2020, 175(15): 16-25.
13. An X, Kuang D, Guo X, et al. A deep learning method for classification of EEG data based on motor imagery// International Conference on Intelligent Computing (ICIC). Taiyuan: Springer, 2014: 203-210.
14. 褚亚奇, 朱波, 赵新刚, 等. 基于时空特征学习卷积神经网络的运动想象脑电解码方法. 生物医学工程学杂志, 2021, 38(1): 1-9.
15. Kim J, Park Y, Chung W. Transform based feature construction utilizing magnitude and phase for convolutional neural network in EEG signal classification// 2020 8th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2020: 1-4.
16. Wu Hao, Niu Yi, Li Fu, et al. A parallel multiscale filter bank convolutional neural networks for motor imagery EEG classification. Front Neurosci, 2019, 26(13): 1275.
17. Michielli N, Acharya U R, Molinari F. Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals. Comput Biol Med, 2019, 106: 71-81.
18. Wang P, Jiang A, Liu X, et al. LSTM-based EEG classification in motor imagery tasks. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(11): 2086-2095.
19. Sun Biao, Zhao Xing, Zhang Han, et al. EEG motor imagery classification with sparse spectrotemporal decomposition and deep learning. IEEE T Autom Sci Eng, 2020, 18(22): 541-551.
20. Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module// European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 1-17.
21. Chollet F. Xception: Deep learning with depthwise separable convolutions// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 1800-1807.
22. Chaudhary S, Taran S, Bajaj V, et al. Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens J, 2019, 19(12): 4494-4500.
23. Donoho D L. Compressed sensing. IEEE T Inform Theory, 2006, 52(4): 1289-1306.
24. Schamberg G, Ba D, Coleman T P. A modularized efficient framework for non-Markov time series estimation. IEEE T Signal Proces, 2018, 66(12): 3140-3154.
25. Boyd S, Parikh N, Chu E, et al. Distributed optimization and statistical learning via the alter nating direction method of multipliers. Boston-Delft: Now Foundations and Trends, 2011.
26. Hu Yaohua, Li Chong, Meng Kaiwen, et al. Group sparse optimization via l (p), (q) regularization. J Mach Learn Res, 2017, 18(1): 960-1011.
27. Cao Yudong, Ding Yifei, Jia Minping, et al. A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings. Reliab Eng Syst Saf, 2021, 215: 107813.
28. Kingma D P, Ba J. Adam: a method for stochastic optimization// The 3rd International Conference on Learning Representations, San Diego, 2015. arXiv, 2015: 1412.6980.
29. Schirrmeister R T, Springenberg J T, Fiederer L D J, et al. Deeplearning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp, 2017, 38(11): 5391-5420.
30. Amin S U, Alsulaiman M, Muhammad G, et al. Deep learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Gener Comput Syst, 2019, 101: 542-554.
31. Lawhern V J, Solon A J, Waytowich N R, et al. EEGNet: a compact convolutional neural network for EEG-based braincomputer interfaces. J Neural Eng, 2018, 15(5): 056013.1-056013.17.
32. 何群, 杜硕, 张园园, 等. 融合单通道框架及多通道框架的运动想象分类. 仪器仪表学报, 2018, 39(9): 20-29.
33. 张绍荣, 赵紫宁, 莫云, 等. 特征提取对通道选择方法的影响研究. 国外电子测量技术, 2020, 39(9): 1-6.

Journal of Biomedical Engineering

Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention

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