基于眼动信号的便携式无线智能交互系统设计
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摘要
肢体运动障碍患者由于无法控制四肢操控现代化的电子设备(如手机、平板等)与外界交流,因此很难融入信息化的社会。设计一个基于眼动信号的便携式无线智能交互系统,帮助使用者利用自身眼动信号控制电子设备,实现与外界的沟通。该系统包含模拟电路和数字电路两部分,模拟电路实现眼动信号的滤波、放大等处理,数字电路将模拟信号转换为数字信号,并实现对信号的实时分析处理,主要包括采用基于短时能量的端点获得信号活动段的起止位置,并将位置、幅值、波峰数等作为信号特征选择量,然后采用过零点分析和动态阈值方法识别不同类型的眼动信号,利用状态机定义不同类型的眼部信号与电子设备中鼠标动作的对应关系。选择15名使用者进行试验测试,要求被试根据随机指令控制眼部实现5个不同动作,包括眼球向上看、向下看、向左看、向右看、主动眨眼,每个动作共执行100次。测试结果表明,每个眼部动作平均识别准确率大于94%,最低平均信息传输率大于22 bits/min,因此能够利用眼动信号代替鼠标实现对电子设备的控制,实现字符输入、拨打电话、听音乐以及浏览网页等功能。
Patients with motor impairment are unable to control modern electronic devices(mobile phones, iPAD, etc.) to communicate with the outside world. It is difficult for them to integrate into information-based society. In this paper, a portable wireless intelligent interactive system was designed to help users control electronic devices with their own eye movement signals to help them communicate with the outside world. The system was made up of analog and digital circuits. The analog circuit was used for filtering and amplification. The digital circuit was used to convert analog signals to digital signals, and then the digital signals were analyzed and processed in real time. It included using short-term energy endpoints to obtain starting and ending positions of signal activity segments, selecting location, amplitude, and wave peak number as signal characteristics, and then using zero-crossing analysis and dynamic threshold method to identify different types of eye movement signals. At last a state machine was used to define the corresponding relationship between different types of eye signals and mouse movements in the electronic devices. Fifteen subjects were enrolled for the test, they were required to control five different eye movements according to random instructions, including looking up, down, left, right and active blinking, and each action was performed 100 times in total. Results showed that the average accuracy of each eye movement was more than 94%, and the minimum average information transfer rate was more than 22 bits/min. In conclusion, this system could use the eye movement to replace the mouse to control electronic devices, realizing functions of character input, making phone calls, listening to music, and browsing the web.
Patients with motor impairment are unable to control modern electronic devices(mobile phones, iPAD, etc.) to communicate with the outside world. It is difficult for them to integrate into information-based society. In this paper, a portable wireless intelligent interactive system was designed to help users control electronic devices with their own eye movement signals to help them communicate with the outside world. The system was made up of analog and digital circuits. The analog circuit was used for filtering and amplification. The digital circuit was used to convert analog signals to digital signals, and then the digital signals were analyzed and processed in real time. It included using short-term energy endpoints to obtain starting and ending positions of signal activity segments, selecting location, amplitude, and wave peak number as signal characteristics, and then using zero-crossing analysis and dynamic threshold method to identify different types of eye movement signals. At last a state machine was used to define the corresponding relationship between different types of eye signals and mouse movements in the electronic devices. Fifteen subjects were enrolled for the test, they were required to control five different eye movements according to random instructions, including looking up, down, left, right and active blinking, and each action was performed 100 times in total. Results showed that the average accuracy of each eye movement was more than 94%, and the minimum average information transfer rate was more than 22 bits/min. In conclusion, this system could use the eye movement to replace the mouse to control electronic devices, realizing functions of character input, making phone calls, listening to music, and browsing the web.
引文
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[2] 丁其川,熊安斌,赵新刚,et al.基于表面肌电的运动意图识别方法研究及应用综述[J].自动化学报,2016,42(1):13-25.
[3] Mcfarland DJ,Mccane LM,David SV,et al.Spatial filter selection for EEG-based communication[J].Electroencephalography & Clinical Neurophysiology,1997,103(3):386-394.
[4] Pfurtscheller G,Flotzinger D,Kalcher J.Brain-computer interface—A new communication device for handicapped persons[J].Journal of Microcomputer Applications,1993,16(3):293-299.
[5] Duncan JC,Donchin E.On quantifying surprise:the variation of event-related potentials with subjective probability[J].Psychophysiology,2010,14(5):456-467.
[6] Panicker RC,Puthusserypady S,Sun Y.An Asynchronous P300 BCI with SSVEP-based control state detection[J].IEEE Transactions on Biomedical Engineering,2011,58(6):1781-1788.
[7] DaCruz JN,Wan F,Wong CM,et al.Adaptive time-window length based on online performance measurement in SSVEP-based BCIs[J].Neurocomputing,2015,149(1):93-99.
[8] Farwell LA,Donchin E.Talking off the top of your head:toward a mental prosthesis utilizing event-related brain potentials[J].Electroencephalography and Clinical Neurophysiology,1989,70(6):510-523.
[9] Yu T,Li Y,Long J,et al.Surfing the internet with a BCI mouse[J].Journal of Neural Engineering,2012,9(3):036012.
[10] Yangsong Z,Peng X,Tiejun L,et al.Multiple frequencies sequential coding for SSVEP-Based brain-computer interface[J].PLoS one,2012,7(3):e29519.
[11] Lin K,Wang Y,Gao X.Time-frequency joint coding method for boosting information transfer rate in an SSVEP based BCI system[C]// 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Florida:IEEE,2016:5873-5876.
[12] Kamel N,Aamir SM.EEG/ERP Analysis:Methods and Applications[M].2th Edition,Canada:Apple Academic Press Inc,2014:20-25.
[13] Saponas TS,Tan DS,Dan M,et al.Making Muscle-Computer Interfaces More Practical[C]// Proceedings of the 28th International Conference on Human Factors in Computing Systems.Atlanta:CHI,2010:851-855.
[14] Fleischer C,Hommel G.A Human--exoskeleton interface utilizing electromyography[J].IEEE Transactions on Robotics,2008,24(4):872-882.
[15] 张洁,崔丽英.肌萎缩侧索硬化患者的重复神经电刺激特点[J].中国神经免疫学和神经病学杂志,2016,23(1):25-28.
[16] 李勃.脑机接口技术研究综述[J].数字通信,2013,40(4):5-8.
[17] 张丹,李佳蔚.探索思维的力量探索思维的力量:脑机接口研究现状与展望[J].科技导报,2017,35(9):62-67.
[18] Hochberg LR,Bacher D,Jarosiewicz B,et al.Reach and grasp by people with tetraplegia using a neurally controlled robotic arm[J].Nature,2013,485(7398):372-375.
[19] 郜东瑞,甘玉龙,李鹏霄,等.基于眼电的智能输入系统研究[J].中国生物医学工程学报,2015,34(6):662-669.
[20] Ghaheri H,Ahmadyfard A.Extracting common spatial patterns from EEG time segments for classifying motor imagery classes in a Brain Computer Interface (BCI) [J].Scientia Iranica,2013,20(6):2061-2072.
[21] 张焕,乔晓艳.多任务运动想象脑电特征的融合分类研究[J].传感技术学报,2016,29(6):802-807.
[22] 欧祖宏.林铭铎.基于SSVEP与运动想象的实时脑控阿凡达系统[J].计算机与现代化,2018,269(1):36-39.
[23] Pandarinath C,Nuyujukian P,Blabe CH,et al.High performance communication by people with paralysis using an intracortical brain-computer interface[J].Human Biology and Medicine,2017,6(1):e18554.
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