1. |
Wolpaw J R, Birbaumer N, Mcfarland D J, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
|
2. |
Herweg A, Gutzeit J, Kleih S, et al. Wheelchair control by elderly participants in a virtual environment with a brain-computer interface (BCI) and tactile stimulation. Biol Psychol, 2016, 121: 117-124.
|
3. |
Saravanakumar D, Reddy M R. A high performance hybrid SSVEP based BCI speller system. Adv Eng Inform, 2019, 42: 100994.
|
4. |
Schloegl A, Lee F, Bischof H, et al. Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J Neural Eng, 2005, 2(4): 14-22.
|
5. |
Hill N J, Lal T N, Schroeder M, et al. Classifying event-related desynchronization in EEG, ECoG and MEG signals// The 28th Annual Symposium of the Gernan Association for Pattern Recognition, Berlin: Springer Berlin Heidelberg, 2006, 4174: 404-413.
|
6. |
Singh A, Hussain A A, Lal S, et al. A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface. Sensors, 2021, 21(6): 2173.
|
7. |
Chu Yaqi, Zhao Xingang, Zou Yijun, et al. Decoding multiclass motor imagery EEG from the same upper limb by combining riemannian geometry features and partial least squares regression. J Neural Eng, 2020, 17(4): 046029.
|
8. |
Das A K, Suresh S. An effect-size based channel selection algorithm for mental task classification in brain computer interface// The IEEE international conference on systems, man, and cybernetics, Hongkong: IEEE, 2015: 3140-3145.
|
9. |
Qi F F, Wu W, Yu Z L, et al. Spatio temporal-filtering-based channel selection for single-trial EEG classification. IEEE T Cybernetics, 2021, 51(2): 558-567.
|
10. |
Park Y, Chung W. Selective feature generation method based on time domain parameters and correlation coefficients for filter-bank- CSP BCI systems. Sensors, 2019, 19(17): 3769.
|
11. |
付荣荣, 田永胜, 鲍甜怡. 基于稀疏共空间模式和Fisher判别的单次运动想象脑电信号识别方法. 生物医学工程学杂志, 2019, 36(6): 911-915, 923.
|
12. |
骆金晨, 姜月, 胡秀枋, 等. 基于多特征融合的多分类运动想象脑电信号识别研究. 生物信息学, 2020, 18(3): 176-185.
|
13. |
郜东瑞, 周晖, 冯李逍, 等. 基于特征融合和粒子群优化算法的运动想象脑电信号识别方法. 电子科技大学学报, 2021, 50(3): 467-475.
|
14. |
汲继跃, 佘青山, 张启中, 等. 最优区域共空间模式的运动想象脑电信号分类方法. 传感技术学报, 2020, 33(1): 34-39.
|
15. |
Goldberger A L, Amaral L A N, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet-components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): 215-220.
|
16. |
Schalk G, Mcfarland D J, Hinterberger T, et al. BCI2000: a general-purpose, brain-computer interface (BCI) system. IEEE T Bio-Med Eng, 2004, 51(6): 1034-1043.
|
17. |
Varsehi H, Firoozabadi S M P. An EEG channel selection method for motor imagery based brain-computer interface and neurofeedback using Granger causality. Neural Networks, 2021, 133: 193-206.
|
18. |
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.
|
19. |
Hou Y M, Zhou L, Jia S Y, et al. A novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN. J Neural Eng, 2020, 17(1): 016048.
|
20. |
徐欣, 王娜. 四类运动想象脑电信号的特征提取与分类. 南京邮电大学学报: 自然科学版, 2017, 37(6): 18-22.
|
21. |
马满振, 郭理彬, 苏奎峰. 基于多类运动想象任务的EEG信号分类研究. 计算机测量与控制, 2017, 25(10): 232-239.
|
22. |
Robnik-Sikonja M, Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn, 2003, 53(1-2): 23-69.
|
23. |
张小内, 翟文鹏, 侯惠让, 等. 基于ReliefF-Pearson的嗅觉脑电通道选择. 电子信息学报, 2021, 43(7): 2032-2037.
|
24. |
Mishuhina V, Jiang Xudong. Feature weighting and regularization of common spatial patterns in EEG-based motor imagery BCI. IEEE Signal Proc Let, 2018, 25(6): 783-787.
|
25. |
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.
|
26. |
Fu Rongrong, Han Mengmeng, Tian Yongsheng, et al. Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis. J Neurosci Meth, 2020, 343: 108833.
|
27. |
Luo Jing, Gao Xing, Zhu Xiaobei, et al. Motor imagery EEG classification based on ensemble support vector learning. Comput Meth Prog Bio, 2020, 193: 105464.
|
28. |
Tolic M, Jovic F. Classification of wavelet transformed EEG signals with neural network for imagined mental and motor tasks. Kinesiology, 2013, 45(1) :130-138.
|
29. |
Kim Y, Ryu J, Kim K K, et al. Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns. Comput Intel Neurosc. 2016, 2016: 1489692.
|
30. |
Dose H, Moller J S, Lversen H K, et al. An end-to-end deep learning approach to MI-EEG signal classification for BCIs. Expert Syst Appl, 2018, 114: 532-542.
|