A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual Targets

被引:5
|
作者
Song, Xiyu [1 ]
Zeng, Ying [1 ,2 ]
Tong, Li [1 ]
Shu, Jun [1 ]
Yang, Qiang [3 ]
Kou, Jian [4 ]
Sun, Minghua [5 ]
Yan, Bin [1 ]
机构
[1] PLA Strateg Support Force Informat Engn Univ, Henan Key Lab Imaging & Intelligent Proc, Zhengzhou, Peoples R China
[2] Univ Elect Sci & Technol China, Clin Hosp, Chengdu Brain Sci Inst, MOE Key Lab Neuro Informat, Chengdu, Peoples R China
[3] Xian Satellite Control Ctr, Hangzhou, Peoples R China
[4] PLA 32317 Force, Urumqi, Peoples R China
[5] Zhengzhou Univ, Cent China Fuwai Hosp, Henan Prov Peoples Hosp, Dept Radiol, Zhengzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
SINGLE-TRIAL EEG;
D O I
10.1155/2022/4752450
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The superiority of collaborative brain-computer interface (cBCI) in performance enhancement makes it an effective way to break through the performance bottleneck of the BCI-based dynamic visual target detection. However, the existing cBCIs focus on multi-mind information fusion with a static and unidirectional mode, lacking the information interaction and learning guidance among multiple agents. Here, we propose a novel cBCI framework to enhance the group detection performance of dynamic visual targets. Specifically, a mutual learning domain adaptation network (MLDANet) with information interaction, dynamic learning, and individual transferring abilities is developed as the core of the cBCI framework. MLDANet takes P3-sSDA network as individual network unit, introduces mutual learning strategy, and establishes a dynamic interactive learning mechanism between individual networks and collaborative decision-making at the neural decision level. The results indicate that the proposed MLDANet-cBCI framework can achieve the best group detection performance, and the mutual learning strategy can improve the detection ability of individual networks. In MLDANet-cBCI, the F1 scores of collaborative detection and individual network are 0.12 and 0.19 higher than those in the multi-classifier cBCI, respectively, when three minds collaborate. Thus, the proposed framework breaks through the traditional multi-mind collaborative mode and exhibits a superior group detection performance of dynamic visual targets, which is also of great significance for the practical application of multi-mind collaboration.
引用
收藏
页数:12
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