边 琰,赵 丽,孙 永.ASMI-BCI 特征调制及分类性能研究[J].电子测量与仪器学报,2022,36(3):224-230
ASMI-BCI 特征调制及分类性能研究
Research on feature modulation and classification performance of ASMI-BCI
  
DOI:
中文关键词:  脑-机接口  运动想象  听觉辅助刺激  特征调制  分类性能
英文关键词:brain-computer interface ( BCI )  motor imagery ( MI )  auditory assistant stimulus  feature modulation  classification performance
基金项目:天津市应用基础与前沿技术研究计划项目(18JCYBJC88200)资助
作者单位
边 琰 1.天津职业技术师范大学天津市信息传感与智能控制重点实验室 
赵 丽 1.天津职业技术师范大学天津市信息传感与智能控制重点实验室 
孙 永 1.天津职业技术师范大学天津市信息传感与智能控制重点实验室 
AuthorInstitution
Bian Yan 1.Tianjin Information Sensing & Intelligent Control Key Lab, Tianjin University of Technology and Education 
Zhao Li 1.Tianjin Information Sensing & Intelligent Control Key Lab, Tianjin University of Technology and Education 
Sun Yong 1.Tianjin Information Sensing & Intelligent Control Key Lab, Tianjin University of Technology and Education 
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中文摘要:
      基于运动想象(MI)的脑-机接口(BCI)近年来被应用于肢体运动功能的可塑性康复。 采用视觉辅助刺激可以有效增强 MI-BCI 系统的分类性能,但视觉障碍患者无法使用。 因此本文设计了基于听觉辅助刺激的 ASMI-BCI,发现动态声音辅助刺激 可以提高大脑运动相关皮层的兴奋性,增强系统的可分性特征。 10 名在校大学生(5 男 5 女,平均 22. 6 岁) 3 类实验范式(CSW、C-DA、C-DV)的平均结果表明,C-SW 范式分类正确率最低、C-DA 次之、C-DV 范式正确率最高。 听觉辅助刺激范式的最优 分类正确率可达 76. 03%,相比传统 MI-BCI 范式显著性提升了 8. 83%,且 60%的被试使用该范式的分类正确率可高于 70%。 使 用动态听觉辅助刺激范式可以为视觉障碍患者提供一种特征调制和 BCI 性能增强的新模式、新方法。
英文摘要:
      Brain-computer interface (BCI) based on motor imagery (MI) has been applied to the plasticity rehabilitation of limb motor function in recent years. Visual assistant stimulus can improve the classification performance of MI-BCI. However, for users with impaired visual system, visual assistant stimulus cannot be used. Therefore, this paper designs ASMI-BCI based on auditory assistant stimulus. It has been found that dynamic acoustic assistant stimulus could improve the excitability of motor related cortex, and enhanced the separability features of related frequency bands. The average classification results of the three experimental paradigms (C-SW, CDA, C-DV ) for 10 college students (5 males and 5 females, with an average age of 22. 6 years old) showed that the classification accuracy of C-SW paradigm was the lowest, followed by C-DA, and the accuracy of C-DV paradigm was the highest. The optimal classification accuracy of the auditory assistant stimulus paradigm was 76. 03% and the average classification accuracy was significantly improved by 8. 83% compared with the traditional MI-BCI paradigm. For 60% of the subjects, the classification accuracy of this paradigm can reach higher than 70%. The dynamic auditory assistant stimulus paradigm can provide a new pattern and method of feature modulation and BCI performance enhancement for patients with visual impairment.
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