基于脑机接口的脑卒中患者语音看护系统
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摘要
脑卒中患者的语言障碍使得他们无法表达自己的意图,给患者的治疗和生活带来了巨大的困难。为了改善患者的语言状况,便于家人和看护人员的看护,本研究利用脑机接口技术设计实现了一种服务于脑卒中患者的语音看护系统。该系统利用Alpha波实现异步的工作方式,患者可以自主地选择何时启动系统运行;利用对稳态视觉诱发电位(steady-state visual evoked potential,SSVEP)信号的分析实现患者语言意图的识别;利用语音合成技术完成患者语言的语音表达输出。该系统的在线实验结果证实了该系统的可行性与有效性,四名被试者可以利用Alpha波控制系统的启动与空闲,系统启动后再利用SSVEP信号控制自身的表达意图,控制准确率高达90%以上。
The language barrier of stroke patients makes them unable to express their language intention,which makes great difficulties to the treatment and life of them. In order to improve the language condition of patients and facilitate the care of family members and nurses,we used brain-computer interface technology to design a speech care system for stroke patients. This system used Alpha wave to realize asynchronous working mode,and patients could choose when to start the system with this working mode. The analysis of SSVEP signals was used to identify the patient' s language intention. The speech output of patient was achieved by speech synthesis.The online experiment results of the system confirmes the feasibility and effectiveness of this system. Four subjects can use the Alpha wave control system to start and idle,and the SSVEP signal is used to control their expression intention after the system is started,with the control accuracy over 90%.
The language barrier of stroke patients makes them unable to express their language intention,which makes great difficulties to the treatment and life of them. In order to improve the language condition of patients and facilitate the care of family members and nurses,we used brain-computer interface technology to design a speech care system for stroke patients. This system used Alpha wave to realize asynchronous working mode,and patients could choose when to start the system with this working mode. The analysis of SSVEP signals was used to identify the patient' s language intention. The speech output of patient was achieved by speech synthesis.The online experiment results of the system confirmes the feasibility and effectiveness of this system. Four subjects can use the Alpha wave control system to start and idle,and the SSVEP signal is used to control their expression intention after the system is started,with the control accuracy over 90%.
引文
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[12]万柏坤,綦宏志,赵丽,等.基于脑电Alpha波的脑-机接口控制实验[J].天津大学学报,2006,39(8):978-984.
[13]高诺,翟文文,鲁加兴,等.基于稳态视觉诱发电位脑机接口的智能轮椅系统研究[J].生物医学工程研究,2018,37(1):6-10.
[14]高诺,翟文文,杨玉娜. MSI和CCA算法对稳态视觉诱发电位信号分类的比较研究[J].信号处理,2018,34(8):984-990.
[15]Bin G Y,Gao X R,Yan Z,et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method[J]. Journal of Neural Engineering,2009,6(4):1771-1779.
[2]胡进,侯增广,陈翼雄,等.下肢康复机器人及其交互控制方法[J].自动化学报,2014,40(11):2377-2390.
[3]Nijboer F,Sellers E W,Mellinger J,et al. A P300-based braincomputer interface for people with amyotrophic lateral sclerosis[J].Clinical Neurophysiology,2008,119(8):1909-1916.
[4]吴边,苏煜,张剑慧,等.基于P300电位的新型BCI中文输入虚拟键盘系统[J].电子学报,2009,37(8):1733-1738.
[5]张剑慧.基于P300的在线脑机接口系统[D].浙江大学,2010.
[6]Minett J W,Peng G,Zhou L,et al. An assistive communication brain-computer interface for chinese text input[C]∥International Conference on Bioinformatics and Biomedical Engineering. IEEE,2010:1-4.
[7]孙红艳,金晶,张宇,等.基于人脸范式的P300拼写系统的参数优化[J].中国生物医学工程学报,2018,37(1):25-32.
[8]邓志东,李修全,郑宽浩,等.一种基于SSVEP的仿人机器人异步脑机接口控制系统[J].机器人,2011,33(2):129-135.
[9]Pan J,Li Y,Zhang R,et al. Discrimination between control and idle states in asynchronous SSVEP-based brain switches:a pseudo-key-based approach[J]. IEEE Transactions on Neural Systems&Rehabilitation Engineering,2013,21(3):435-443.
[10]Borisoff J F,Mason S G,Birch G E. Brain interface research for asynchronous control applications[J]. IEEE Transactions on Neural Systems&Rehabilitation Engineering,2006,14(2):160-164.
[11]Panicker R C,Puthusserypady S,Pryana A P,et al. Asynchronous P300 BCI:SSVEP-based control state detection[C]∥European Signal Processing Conference,IEEE,2010:934-938.
[12]万柏坤,綦宏志,赵丽,等.基于脑电Alpha波的脑-机接口控制实验[J].天津大学学报,2006,39(8):978-984.
[13]高诺,翟文文,鲁加兴,等.基于稳态视觉诱发电位脑机接口的智能轮椅系统研究[J].生物医学工程研究,2018,37(1):6-10.
[14]高诺,翟文文,杨玉娜. MSI和CCA算法对稳态视觉诱发电位信号分类的比较研究[J].信号处理,2018,34(8):984-990.
[15]Bin G Y,Gao X R,Yan Z,et al. An online multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis method[J]. Journal of Neural Engineering,2009,6(4):1771-1779.