Classification algorithms of error-related potentials in brain-computer interface_Journal of Biomedical Engineering

Authors：

SUN Jinsong ¹ , Jung Tzyy-Ping ^1,2,3 ,  XIAO Xiaolin ¹ , MENG Jiayuan ¹ , XU Minpeng ^1,2 , MING Dong ^1,2

1. Academy of Medical Engineering and Translational Medicine. Tianjin University, Tianjin 300072, P.R.China;
2. School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, P.R.China;
3. University of California and Swartz Center for Computational Neuroscience, California 92093, America;

Corresponding?author：

XIAO Xiaolin, Email: xiaoxiao0@tju.edu.cn

Keywords：

brain-computer interface; error-related potentials; pattern recognition; single trial recognition

DOI：

10.7507/1001-5515.202012013

Video：

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Abstract Full text Figures/Tables Video References Cited by

Error self-detection based on error-related potentials (ErrP) is promising to improve the practicability of brain-computer interface systems. But the single trial recognition of ErrP is still a challenge that hinters the development of this technology. To assess the performance of different algorithms on decoding ErrP, this paper test four kinds of linear discriminant analysis algorithms, two kinds of support vector machines, logistic regression, and discriminative canonical pattern matching (DCPM) on two open accessed datasets. All algorithms were evaluated by their classification accuracies and their generalization ability on different sizes of training sets. The study results show that DCPM has the best performance. This study shows a comprehensive comparison of different algorithms on ErrP classification, which could give guidance for the selection of ErrP algorithm.

Citation： SUN Jinsong, Jung Tzyy-Ping, XIAO Xiaolin, MENG Jiayuan, XU Minpeng, MING Dong. Classification algorithms of error-related potentials in brain-computer interface. Journal of Biomedical Engineering, 2021, 38(3): 463-472. doi: 10.7507/1001-5515.202012013 Copy

1.	Rashid M, Sulaiman N, Abdul Majeed A P P, et al. Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review. Front Neurorobot, 2020, 14(6): 25.
2.	Khoshnevis S A, Sankar R. Applications of higher order statistics in electroencephalography signal processing: a comprehensive survey. IEEE Rev Biomed Eng, 2020, 13: 169-183.
3.	Gehring W, Goss B, Coles M, et al. A neural system for error detection_psych science. Psychol Sci, 1993, 4(6): 1-6.
4.	Chavarriaga R, Millan J D. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(4): 381-388.
5.	Schalk G, Wolpaw J R, Mcfarland D J, et al. EEG-based communication: presence of an error potential. Clin Neurophysiol, 2000, 111(12): 2138-2144.
6.	Zhang H, Chavarriaga R, Khaliliardali Z, et al. EEG-based decoding of error-related brain activity in a real-world driving task. J Neural Eng, 2015, 12(6): 066028.
7.	Spüler M, Niethammer C. Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity. Front Hum Neurosci, 2015, 9: 155.
8.	Kim S K, Kirchner E A, Stefes A, et al. Intrinsic interactive reinforcement learning-using error-related potentials for real world human-robot interaction. Sci Rep, 2017, 7(1): 17562.
9.	Zhang Yue, Chen Weihai, Lin Chunliang, et al. Research on command confirmation unit based on motor imagery EEG signal decoding feedback in brain-computer interface//2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore: IEEE, 2018: 1923-1928.
10.	Wirth C, Dockree P M, Harty S, et al. Towards error categorisation in BCI: single-trial EEG classification between different errors. J Neural Eng, 2019, 17(1): 016008.
11.	Kim S K, Kirchner E A. Classifier transferability in the detection of error related potentials from observation to interaction//2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester: IEEE, 2013: 3360-3365.
12.	Margaux P, Emmanuel M, Daligault S, et al. Objective and subjective evaluation of online error correction during P300-Based spelling. Advances in Human-Computer Interaction, 2012(6): 1687-5893.
13.	Fisher R A. The use of multiple measurements in taxonomic problems. Ann Eugen, 1936, 7(2): 179-188.
14.	Krusienski D J, Sellers E W, Mcfarland D J, et al. Toward enhanced P300 speller performance. J Neurosci Methods, 2008, 167(1): 15-21.
15.	Blankertz B, Lemm S, Treder M, et al. Single-trial analysis and classification of ERP components-a tutorial. Neuroimage, 2011, 56(2): 814-825.
16.	Hoffmann U, Vesin J M, Ebrahimi T, et al. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods, 2008, 167(1): 115-125.
17.	Bhattacharyya S, Konar A, Tibarewala D N, et al. A generic transferable EEG decoder for online detection of error potential in target selection. Front Neurosci, 2017, 11: 226.
18.	Lucas C, Clay D, Gerson A D, et al. Recipes for the linear analysis of EEG. Neuroimage, 2005, 28(2): 326-341.
19.	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.
20.	Hui K, Teoh E K, Jian G, et al. Two dimensional fisher discriminant analysis: forget about small sample size problem// 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia: IEEE, 2005: 761-764.

1. Rashid M, Sulaiman N, Abdul Majeed A P P, et al. Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review. Front Neurorobot, 2020, 14(6): 25.
2. Khoshnevis S A, Sankar R. Applications of higher order statistics in electroencephalography signal processing: a comprehensive survey. IEEE Rev Biomed Eng, 2020, 13: 169-183.
3. Gehring W, Goss B, Coles M, et al. A neural system for error detection_psych science. Psychol Sci, 1993, 4(6): 1-6.
4. Chavarriaga R, Millan J D. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(4): 381-388.
5. Schalk G, Wolpaw J R, Mcfarland D J, et al. EEG-based communication: presence of an error potential. Clin Neurophysiol, 2000, 111(12): 2138-2144.
6. Zhang H, Chavarriaga R, Khaliliardali Z, et al. EEG-based decoding of error-related brain activity in a real-world driving task. J Neural Eng, 2015, 12(6): 066028.
7. Spüler M, Niethammer C. Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity. Front Hum Neurosci, 2015, 9: 155.
8. Kim S K, Kirchner E A, Stefes A, et al. Intrinsic interactive reinforcement learning-using error-related potentials for real world human-robot interaction. Sci Rep, 2017, 7(1): 17562.
9. Zhang Yue, Chen Weihai, Lin Chunliang, et al. Research on command confirmation unit based on motor imagery EEG signal decoding feedback in brain-computer interface//2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore: IEEE, 2018: 1923-1928.
10. Wirth C, Dockree P M, Harty S, et al. Towards error categorisation in BCI: single-trial EEG classification between different errors. J Neural Eng, 2019, 17(1): 016008.
11. Kim S K, Kirchner E A. Classifier transferability in the detection of error related potentials from observation to interaction//2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester: IEEE, 2013: 3360-3365.
12. Margaux P, Emmanuel M, Daligault S, et al. Objective and subjective evaluation of online error correction during P300-Based spelling. Advances in Human-Computer Interaction, 2012(6): 1687-5893.
13. Fisher R A. The use of multiple measurements in taxonomic problems. Ann Eugen, 1936, 7(2): 179-188.
14. Krusienski D J, Sellers E W, Mcfarland D J, et al. Toward enhanced P300 speller performance. J Neurosci Methods, 2008, 167(1): 15-21.
15. Blankertz B, Lemm S, Treder M, et al. Single-trial analysis and classification of ERP components-a tutorial. Neuroimage, 2011, 56(2): 814-825.
16. Hoffmann U, Vesin J M, Ebrahimi T, et al. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods, 2008, 167(1): 115-125.
17. Bhattacharyya S, Konar A, Tibarewala D N, et al. A generic transferable EEG decoder for online detection of error potential in target selection. Front Neurosci, 2017, 11: 226.
18. Lucas C, Clay D, Gerson A D, et al. Recipes for the linear analysis of EEG. Neuroimage, 2005, 28(2): 326-341.
19. 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.
20. Hui K, Teoh E K, Jian G, et al. Two dimensional fisher discriminant analysis: forget about small sample size problem// 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia: IEEE, 2005: 761-764.

Journal of Biomedical Engineering

Classification algorithms of error-related potentials in brain-computer interface

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