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Vis Inf
Vis Inf  2020, Vol. 4 Issue (1): 1-7    DOI: 10.1016/j.visinf.2019.12.001
论文     
基于稳态视觉诱发电位识别的虚拟家用电器脑机交互控制接口设计
Fan Zhanga, Hang Yua, Jie Jianga, Zhangye Wangb, Xujia Qina
aCollege of Computer Science, Zhejiang University of Technology, China  bState Key Lab of CAD&CG, Zhejiang University, China
Brain–computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition
Fan Zhanga, Hang Yua, Jie Jianga, Zhangye Wangb, Xujia Qina
aCollege of Computer Science, Zhejiang University of Technology, China  bState Key Lab of CAD&CG, Zhejiang University, China
 全文: PDF 
摘要： 脑机接口是一种新兴的人机交互形式，采用这种交互方式，人脑可通过脑电信号直接控制或操作外部设备。本文利用典型相关分析方法，比较采集的脑电波数据与多组不同频率谐波之间的典型相关系数，选择最大相关系数对应的频率视为刺激频率，实现了稳态视觉诱发电位的识别。 在频率识别过程中，屏幕上显示不同频率闪烁的黑白方块代表不同的控制信号，被试者注视其产生的稳态视觉诱发电位信号，通过算法解析出该信号，确定需要控制的对象，激活相应对象。在算法识别后增加一个分类器，引入投票机制统计识别结果，降低了误激活率。基于改进后的脑机接口算法，我们搭建了一个虚拟家用电器控制系统，在线测试表明，该系统对于三分类问题准确率达到72.84%。
关键词： 脑机接口;  稳态视觉诱发电位;  典型相关分析
    
Abstract: Brain–computer interface is a new form of interaction between humans and machines. This interaction helps the human brain control or operate external devices directly using electroencephalograph (EEG) signals. In this study, we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different frequencies. Then, we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked potentials. Afterward, we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation rate. Finally, we build a virtual household electrical appliance brain–computer control interface, which achieves over 72.84% accuracy for three classification problems.
Key words: Brain–computer interface    Steady-state visually evoked potential    Canonical correlation analysis
出版日期: 2020-01-09
通讯作者: College of Computer Science, Zhejiang University of Technology, China     E-mail: jj@zjut.edu.cn
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Fan Zhang
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引用本文:

Fan Zhang, Hang Yu, Jie Jiang, Zhangye Wang, Xujia Qin. Brain–computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition . Vis Inf, 2020, 4(1): 1-7.

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http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2019.12.001    或    http://www.zjujournals.com/vi/CN/Y2020/V4/I1/1

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