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.
|