An Asynchronous Control Strategy Based on Hybrid BCI
Xu Wei1,2, Zhao Yawei1,2, Qi Hongzhi1,2*
1(College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China) 2(Academy of Medical Engineering and Translational Medicine,Tianjin University, Tianjin 300072, China)
Abstract：Asynchronous control problems of automatically identifying whether users are controlling the system is a focal point of research in BCI. Traditional paradigms induce EEG characteristics by distinguishing different instructions, causing the limitation of the information, so it is difficult to obtain ideal results of asynchronous identification. Aiming to resolve the problem, we developed a hybrid BCI system containingevent-related potential and steady-state visual evoked potential signals to promote the effect. In the study, we collected 19 channel EEG signals from 10 subjects under the control condition when the subjects used the BCI system normally and two idle conditions when subjects moved their eyes out of the screen and when they opened their eyes and rested. In order to identify the different conditions, we extracted the amplitude of the ERP and the correlation coefficient of SSVEP, then we estimated the posterior probability of the control state by ERP and SSVEP features, using a Bayesian method. Using 10 subjects' EEG features, we found out that the hybrid paradigm performed better than a single paradigm, because the differences under the two paradigms coexisted. We achieved the accuracy of 92.1% and an AUC of 0.98 in identifying the control condition and the idle condition. This study demonstrated that hybrid had the ability to improve asynchronous identifying and it was worthy of a further study and development.
徐伟, 赵雅薇, 綦宏志. 基于混合范式的脑-机接口异步控制方法研究[J]. 中国生物医学工程学报, 2020, 39(6): 693-699.
Xu Wei, Zhao Yawei, Qi Hongzhi. An Asynchronous Control Strategy Based on Hybrid BCI. Chinese Journal of Biomedical Engineering, 2020, 39(6): 693-699.
[1] Shih JJ, Krusienski DJ, Wolpaw JR, et al. Brain-computer interfaces in medicine [J]. Mayo Clinic Proceedings, 2012, 87(3): 268-279. [2] Clerc, Maureen. Brain-computer interfaces, principles and practise [J]. Biomedical Engineering Online, 2013, 12(1): 1-4. [3] 杨红宇, 徐鹏, 异步脑机接口技术现状及发展趋势 [J]. 中国生物医学工程学报, 2011, 30(7):74-80. [4] Liu Rong, Wang Yongxuan, Zhang Lin, et al. An FDES-based shared control method for asynchronous brain-actuated robot [J]. IEEE Transactions on Cybernetics, 2016, 46(6): 14-21. [5] Pfurtscheller G, Solis-Escalante T, Ortner R, et al. Self-paced operation of an SSVEP-based orthosis with and without an imagery-based "brain switch:" A feasibility study towards a hybrid BCI [J]. IEEE Transactions on Neural Systems Rehabilitation Engineering, 2010, 18(4): 409-414. [6] Li Yuanqing, Pan Jianhui, Wang Fei, et al. A hybrid BCI system combining P300 and SSVEP and its application to wheelchair control [J]. IEEE Transactions on Biomedical Engineering, 2013, 60(11): 3156-3166. [7] Cao Lei, Li Jie, Ji Hongfei, et al. A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control [J]. Neurosci Methods, 2014, 229(5): 33-43. [8] Chane Y, Jeong J, Jo S. Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI [J]. IEEE Transactions on Robotics, 2012, 28(5): 1131-1144. [9] Dize PF, Orosco L, Laciar E, et al. Attention-level transitory response: a novel hybrid BCI approach [J]. Journal of Neural Engineering, 2015, 12(5): 56-63. [10] Dize PF, Mut VA, Enrique MA, et al. Asynchronous BCI control using high-frequency SSVEP [J]. Journal of Neuroengineering Rehabilitation, 2011, 8(39): 1-8. [11] Duan Feng, Lin Dongxue, Li Wenyu, et al. Design of a multimodal EEG-based hybrid BCI system with visual servo module [J]. IEEE Transactions on Autonomous Mental Development, 2017, 7(4): 1-11. [12] Merino L M, Nayak T, Hall G, et al. Detection of control or idle state with a likelihood ratio test in asynchronous SSVEP-based brain-computer interface systems[C] // International Conference of the IEEE Engineering in Medicine & Biology Society. Piscataway: IEEE, 2016: 1568-1571. [13] Zhang Dan, Huang Bisheng, Li Sillang, et al. An idel-state detection algorithm for SSVEP-based brain-computer interfaces using a maximum evoked response spatial filte [J]. International Journal of Neural Systems, 2015, 25(7): 155-165. [14] Akman AE, Bay OF, Guler I, et al. P300 based asynchronous brain- computer interface for environmental control system [J]. IEEE Journal of Biomedical Health Informatics, 2017, 22(3): 653-663. [15] 陈小刚, 徐圣普. 稳态视觉诱发电位频率响应特性研究 [J]. 北京生物医学工程, 2018, 37(3): 259-264. [16] Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface, Journal of Neural Engineering, 2015, 12(4): 046008. [17] Zhong Linlin, Zhang Changshui, Wu Wei, et al. Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs [J]. IEEE transactions on bio-medical engineering, 2006, 53(2): 2610-2614.
