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.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Jun 25;38(3):409-416. doi: 10.7507/1001-5515.202007059.
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 brain-computer 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% 56.34%), and was significantly higher than that of the average single user (77.31% 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-cBCI research and its future application.
作为最常见的主动式脑机交互范式，运动想象脑机接口（MI-BCI）存在指令集小和准确率低的瓶颈问题，其信息传输率（ITR）和实际应用受到严重限制。在本研究中，我们设计了6类想象动作，采集了19名受试者的脑电图（EEG）信号，研究了协作式脑机接口（cBCI）协作策略对MI-BCI分类性能的影响，比较了不同组规模和融合策略的变化对组多分类性能的影响。结果表明，最合适的组规模为4人，最佳融合策略为决策融合。在此条件下，组的分类准确率达到77%，高于相同组规模下特征融合策略的准确率（77.31%对56.34%），且显著高于平均单用户的准确率（77.31%对44.90%）。本文的研究证明，cBCI协作策略可有效提高MI-BCI分类性能，为MI-cBCI研究及其未来应用奠定了基础。
