基于运动想象脑-机接口的协同策略研究  

Research progress and prospect of collaborative brain-computer interface for group brain collaboration

作　　者：张力新[1] 陈小翠 陈龙 顾斌 王仲朋 明东[1,2] ZHANG Lixin;CHEN Xiaocui;CHEN Long;GU Bin;WANG Zhongpeng;MING Dong(Biomedical Engineering,School of Precision Instruments and Optoelectronics Engineering,Tianjin University,Tianjin 300072,P.R.China;Biomedical Engineering,Academy of Medical Engineering and Translational Medicine,Tianjin University,Tianjin 300072,P.R.China)

机构地区：[1]天津大学精密仪器与光电子工程学院生物医学工程系,天津300072 [2]天津大学医学工程与转化医学研究院生物医学工程系,天津300072

出　　处：《生物医学工程学杂志》2021年第3期409-416,共8页Journal of Biomedical Engineering

基　　金：国家重点研发计划项目(2017YFB1002504);国家自然科学基金重点项目(81630051);天津市科技支撑计划项目(17ZXRGGX00020,16ZXHLSY00270)。

摘　　要：运动想象脑-机接口(MI-BCI)作为最常见的主动式脑-机交互范式,仍存在指令集小、正确率低等瓶颈问题,其信息传输速率(ITR)与实际应用严重受限。本文设计了六指令想象动作,采集了19名被试的脑电信号(EEG),研究协同脑-机接口(cBCI)的协同策略对MI-BCI分类性能的提升效果,对比了不同群体规模、融合策略的变化对群体分类性能的影响。结果表明,最适的群体规模为4人,最佳的融合策略为决策融合,并且在该条件下群体的分类正确率达到了77%,这比相同群体规模下特征融合策略有所提高(77.31%vs.56.34%),并且比单用户的平均水平明显提高(77.31%vs.44.90%)。本研究证明了cBCI协同策略可有效提升MI-BCI分类性能,为MI-cBCI研究及其未来应用奠定了基础。As the most common active brain-computer interaction paradigm,motor imagery brain-computer interface(MI-BCI)suffers from the bottleneck problems of small instruction set and low accuracy,and its information transmission rate(ITR)and practical application are severely limited.In this study,we designed 6-class imagination actions,collected electroencephalogram(EEG)signals from 19 subjects,and studied the effect of collaborative braincomputer interface(cBCI)collaboration strategy on MI-BCI classification performance,the effects of changes in different group sizes and fusion strategies on group multi-classification performance are compared.The results showed that the most suitable group size was 4 people,and the best fusion strategy was decision fusion.In this condition,the classification accuracy of the group reached 77%,which was higher than that of the feature fusion strategy under the same group size(77.31%vs.56.34%),and was significantly higher than that of the average single user(77.31%vs.44.90%).The research in this paper proves that the cBCI collaboration strategy can effectively improve the MI-BCI classification performance,which lays the foundation for MI-c BCI research and its future application.

关 键 词：协同脑-机接口 运动想象 特征融合 决策融合 群体规模 

分 类 号：R318[医药卫生—生物医学工程] TN911.7[医药卫生—基础医学]