Progresses and prospects on frequency recognition methods for steady-state visual evoked potential_Journal of Biomedical Engineering

Authors：

ZHANG Yangsong ^1,2 , XIA Min ¹ , CHEN Ke ² , XU Peng ² ,  YAO Dezhong ^2,3

1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan 621010, P. R. China;
2. MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China;
3. Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, P. R. China;

Corresponding author：

Keywords：

Steady-state visual evoked potential; Electroencephalogram; Frequency recognition; Machine learning; Deep learning

DOI：

10.7507/1001-5515.202102031

Video：

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Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.

Citation： ZHANG Yangsong, XIA Min, CHEN Ke, XU Peng, YAO Dezhong. Progresses and prospects on frequency recognition methods for steady-state visual evoked potential. Journal of Biomedical Engineering, 2022, 39(1): 192-197. doi: 10.7507/1001-5515.202102031 Copy

1.	Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
2.	Yao D, Zhang Y, Liu T, et al. Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn, 2020, 14(4): 425-442.
3.	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.
4.	Chen Y, Yang C, Ye X, et al. Implementing a calibration-free SSVEP-based BCI system with 160 targets. J Neural Eng, 2021, 18(4): 046094.
5.	Zerafa R, Camilleri T, Falzon O, et al. To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs. J Neural Eng, 2018, 15: 051001.
6.	Lin Zhonglin, Zhang Changshui, Wu Wei, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng, 2007, 54(6): 1172-1176.
7.	Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12: 046008.
8.	Ge S, Jiang Y, Wang P, et al. Training-free steady-state visual evoked potential brain-computer interface based on filter bank canonical correlation analysis and spatiotemporal beamforming decoding. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(9): 1714-1723.
9.	Zhang Y, Xu P, Cheng K, et al. Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface. J Neurosci Methods, 2014, 221: 32-40.
10.	Zhang Y, Guo D, Yao D, et al. The extension of multivariate synchronization index method for SSVEP-based BCI. Neurocomputing, 2017, 269: 226-231.
11.	Shao X, Lin M. Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification. Cogn Neurodyn, 2020, 14(5): 689-696.
12.	Wong C M, Wang B, Wang Z, et al. Spatial filtering in SSVEP-based BCIs: unified framework and new improvements. IEEE Trans Biomed Eng, 2020, 67(11): 3057-3072.
13.	Chen Y, Yang C, Chen X, et al. A novel training-free recognition method for SSVEP-based BCIs using dynamic window strategy. J Neural Eng, 2021, 18: 036007.
14.	Liu B, Huang X, Wang Y, et al. BETA: a large benchmark database toward SSVEP-BCI application. Front Neurosci, 2020, 14: 627.
15.	Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A, 2015, 112(44): E6058-E6067.
16.	Jiao Y, Zhang Y, Wang Y, et al. A novel multilayer correlation maximization model for improving CCA-based frequency recognition in SSVEP brain-computer interface. Int J Neural Syst, 2018, 28: 1750039.
17.	Zhang Y, Yin E, Li F, et al. Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(7): 1314-1323.
18.	Wei Qingguo, Zhu Shan, Wang Yijun, et al. Maximum signal fraction analysis for enhancing signal-to-noise ratio of EEG signals in SSVEP-based BCIs. IEEE Access, 2019, 7: 85452-85461.
19.	Zhang Y, Yin E, Li F, et al. Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs. Neural Netw, 2019, 119: 1-9.
20.	Li Z, Liu K, Deng X, et al. Spatial fusion of maximum signal fraction analysis for frequency recognition in SSVEP-based BCI. Biomed Signal Process Control, 2020, 61: 102042.
21.	Wong C M, Wan F, Wang B, et al. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. J Neural Eng, 2020, 17: 016026.
22.	Jiang J, Yin E, Wang C, et al. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J Neural Eng, 2018, 15: 046025.
23.	Zhao J, Zhang W, Wang J H, et al. Decision-making selector (DMS) for integrating CCA-based methods to improve performance of SSVEP-based BCIs. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(5): 1128-1137.
24.	Zhang X, Yao L, Wang X, et al. A survey on deep learning-based non-invasive brain signals:recent advances and new frontiers. J Neural Eng, 2021, 18: 031002.
25.	Waytowich N, Lawhern V J, Garcia J O, et al. Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials. J Neural Eng, 2018, 15: 066031.
26.	Kwak N S, Muller K R, Lee S W. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLoS One, 2017, 12: e0172578.
27.	Ravi A, Beni N H, Manuel J, et al. Comparing user-dependent and user-independent training of CNN for SSVEP BCI. J Neural Eng, 2020, 17: 026028.
28.	Dang W, Li M, Lv D, et al. MHLCNN: multi-harmonic linkage CNN model for SSVEP and SSMVEP signal classification. IEEE T Circuits-II, 2021, 2021: 3091803.
29.	Li Y, Xiang J, Kesavadas T. Convolutional correlation analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(12): 2681-2690.
30.	Guney O B, Oblokulov M, Ozkan H. A deep neural network for SSVEP-based brain-computer interfaces. IEEE Trans Biomed Eng, 2021, 2021: 3110440.
31.	Liu Q, Jiao Y, Miao Y, et al. Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA. Neurocomputing, 2020, 378: 36-44.
32.	Nik Aznan N K, Atapour-Abarghouei A, Bonner S, et al. Simulating brain signals: creating synthetic EEG data via neural-based generative models for improved SSVEP classification//2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary, 2019: 1-8.
33.	Liu B, Chen X, Li X, et al. Align and pool for EEG headset domain adaptation (ALPHA) to facilitate dry electrode based SSVEP-BCI. IEEE Trans Biomed Eng, 2021: 3105331.
34.	Wong C M, Wang Z, Wang B, et al. Inter- and intra-subject transfer reduces calibration effort for high-speed SSVEP-based BCIs. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(10): 2123-2135.
35.	Chiang K J, Wei C S, Nakanishi M, et al. Boosting template-based SSVEP decoding by cross-domain transfer learning. J Neural Eng, 2021, 18: 016002.
36.	Gao W, Yu T, Yu J G, et al. Learning invariant patterns based on a convolutional neural network and big electroencephalography data for subject-independent P300 brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1047-1057.
37.	Khok H J, Teck Chang Koh V, Guan Cuntai. Deep multi-task learning for SSVEP detection and visual response mapping//2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Toronto, Canada, 2020: 1280-1285.

1. Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
2. Yao D, Zhang Y, Liu T, et al. Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn, 2020, 14(4): 425-442.
3. 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.
4. Chen Y, Yang C, Ye X, et al. Implementing a calibration-free SSVEP-based BCI system with 160 targets. J Neural Eng, 2021, 18(4): 046094.
5. Zerafa R, Camilleri T, Falzon O, et al. To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs. J Neural Eng, 2018, 15: 051001.
6. Lin Zhonglin, Zhang Changshui, Wu Wei, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng, 2007, 54(6): 1172-1176.
7. Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface. J Neural Eng, 2015, 12: 046008.
8. Ge S, Jiang Y, Wang P, et al. Training-free steady-state visual evoked potential brain-computer interface based on filter bank canonical correlation analysis and spatiotemporal beamforming decoding. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(9): 1714-1723.
9. Zhang Y, Xu P, Cheng K, et al. Multivariate synchronization index for frequency recognition of SSVEP-based brain-computer interface. J Neurosci Methods, 2014, 221: 32-40.
10. Zhang Y, Guo D, Yao D, et al. The extension of multivariate synchronization index method for SSVEP-based BCI. Neurocomputing, 2017, 269: 226-231.
11. Shao X, Lin M. Filter bank temporally local canonical correlation analysis for short time window SSVEPs classification. Cogn Neurodyn, 2020, 14(5): 689-696.
12. Wong C M, Wang B, Wang Z, et al. Spatial filtering in SSVEP-based BCIs: unified framework and new improvements. IEEE Trans Biomed Eng, 2020, 67(11): 3057-3072.
13. Chen Y, Yang C, Chen X, et al. A novel training-free recognition method for SSVEP-based BCIs using dynamic window strategy. J Neural Eng, 2021, 18: 036007.
14. Liu B, Huang X, Wang Y, et al. BETA: a large benchmark database toward SSVEP-BCI application. Front Neurosci, 2020, 14: 627.
15. Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci U S A, 2015, 112(44): E6058-E6067.
16. Jiao Y, Zhang Y, Wang Y, et al. A novel multilayer correlation maximization model for improving CCA-based frequency recognition in SSVEP brain-computer interface. Int J Neural Syst, 2018, 28: 1750039.
17. Zhang Y, Yin E, Li F, et al. Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(7): 1314-1323.
18. Wei Qingguo, Zhu Shan, Wang Yijun, et al. Maximum signal fraction analysis for enhancing signal-to-noise ratio of EEG signals in SSVEP-based BCIs. IEEE Access, 2019, 7: 85452-85461.
19. Zhang Y, Yin E, Li F, et al. Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs. Neural Netw, 2019, 119: 1-9.
20. Li Z, Liu K, Deng X, et al. Spatial fusion of maximum signal fraction analysis for frequency recognition in SSVEP-based BCI. Biomed Signal Process Control, 2020, 61: 102042.
21. Wong C M, Wan F, Wang B, et al. Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs. J Neural Eng, 2020, 17: 016026.
22. Jiang J, Yin E, Wang C, et al. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J Neural Eng, 2018, 15: 046025.
23. Zhao J, Zhang W, Wang J H, et al. Decision-making selector (DMS) for integrating CCA-based methods to improve performance of SSVEP-based BCIs. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(5): 1128-1137.
24. Zhang X, Yao L, Wang X, et al. A survey on deep learning-based non-invasive brain signals:recent advances and new frontiers. J Neural Eng, 2021, 18: 031002.
25. Waytowich N, Lawhern V J, Garcia J O, et al. Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials. J Neural Eng, 2018, 15: 066031.
26. Kwak N S, Muller K R, Lee S W. A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLoS One, 2017, 12: e0172578.
27. Ravi A, Beni N H, Manuel J, et al. Comparing user-dependent and user-independent training of CNN for SSVEP BCI. J Neural Eng, 2020, 17: 026028.
28. Dang W, Li M, Lv D, et al. MHLCNN: multi-harmonic linkage CNN model for SSVEP and SSMVEP signal classification. IEEE T Circuits-II, 2021, 2021: 3091803.
29. Li Y, Xiang J, Kesavadas T. Convolutional correlation analysis for enhancing the performance of SSVEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(12): 2681-2690.
30. Guney O B, Oblokulov M, Ozkan H. A deep neural network for SSVEP-based brain-computer interfaces. IEEE Trans Biomed Eng, 2021, 2021: 3110440.
31. Liu Q, Jiao Y, Miao Y, et al. Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA. Neurocomputing, 2020, 378: 36-44.
32. Nik Aznan N K, Atapour-Abarghouei A, Bonner S, et al. Simulating brain signals: creating synthetic EEG data via neural-based generative models for improved SSVEP classification//2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary, 2019: 1-8.
33. Liu B, Chen X, Li X, et al. Align and pool for EEG headset domain adaptation (ALPHA) to facilitate dry electrode based SSVEP-BCI. IEEE Trans Biomed Eng, 2021: 3105331.
34. Wong C M, Wang Z, Wang B, et al. Inter- and intra-subject transfer reduces calibration effort for high-speed SSVEP-based BCIs. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(10): 2123-2135.
35. Chiang K J, Wei C S, Nakanishi M, et al. Boosting template-based SSVEP decoding by cross-domain transfer learning. J Neural Eng, 2021, 18: 016002.
36. Gao W, Yu T, Yu J G, et al. Learning invariant patterns based on a convolutional neural network and big electroencephalography data for subject-independent P300 brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1047-1057.
37. Khok H J, Teck Chang Koh V, Guan Cuntai. Deep multi-task learning for SSVEP detection and visual response mapping//2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Toronto, Canada, 2020: 1280-1285.

Journal of Biomedical Engineering

Progresses and prospects on frequency recognition methods for steady-state visual evoked potential

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