Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm  

作　　者：Cunbo LI Ning LI Yuan QIU Yueheng PENG Yifeng WANG Lili DENG Teng MA Fali LI Dezhong YAO Peng XU 

机构地区：[1]The Clinical Hospital of Chengdu Brain Science Institute,MOE Key Lab for Neuroinformation and School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu 610054,China [2]School of Intelligent Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450015,China

出　　处：《Virtual Reality & Intelligent Hardware》2022年第1期22-37,共16页虚拟现实与智能硬件（中英文）

基　　金：Supported by the National Natural Science Foundation of China(U19A2082,61961160705,61901077);the National Key Research and Development Plan of China(2017YFB1002501);the Key R&D Program of Guangdong Province,China(2018B030339001).

摘　　要：Background As a novel approach for people to directly communicate with an external device,the study of brain-computer interfaces(BCIs)has become well-rounded.However,similar to the real-world scenario,where individuals are expected to work in groups,the BCI systems should be able to replicate group attributes.Methods We proposed a 4-order cumulants feature extraction method(CUM4-CSP)based on the common spatial patterns(CSP)algorithm.Simulation experiments conducted using motion visual evoked potentials(mVEP)EEG data verified the robustness of the proposed algorithm.In addition,to freely choose paradigms,we adopted the mVEP and steady-state visual evoked potential(SSVEP)paradigms and designed a multimodal collaborative BCI system based on the proposed CUM4-CSP algorithm.The feasibility of the proposed multimodal collaborative system framework was demonstrated using a multiplayer game controlling system that simultaneously facilitates the coordination and competitive control of two users on external devices.To verify the robustness of the proposed scheme,we recruited 30 subjects to conduct online game control experiments,and the results were statistically analyzed.Results The simulation results prove that the proposed CUM4-CSP algorithm has good noise immunity.The online experimental results indicate that the subjects could reliably perform the game confrontation operation with the selected BCI paradigm.Conclusions The proposed CUM4-CSP algorithm can effectively extract features from EEG data in a noisy environment.Additionally,the proposed scheme may provide a new solution for EEG-based group BCI research.

关 键 词：Collaborative brain-computer interface(BCI) Motion visual evoked potentials(mVEP) Steady-state visual evoked potential(SSVEP) Game controlling system 

分 类 号：TN911.7[电子电信—通信与信息系统] TP273[电子电信—信息与通信工程] R318[自动化与计算机技术—检测技术与自动化装置]