典型文献
Research on time-frequency cross mutual of motor imagination data based on multichannel EEG signal
文献摘要：
At present, multi-channel electroencephalogram ( EEG) signal acquisition equipment is used to collect motor imagery EEG data, and there is a problem with selecting multiple acquisition channels. Choosing too many channels will result in a large amount of calculation. Components irrelevant to the task will interfere with the required features, which is not conducive to the real-time processing of EEG data. Using too few channels will result in the loss of useful information and low robustness. A method of selecting data channels for motion imagination is proposed based on the time-frequency cross mutual information ( TFCMI) . This method determines the required data channels in a targeted manner, uses the common spatial pattern mode for feature extraction, and uses support vector ma-chine ( SVM) for feature classification. An experiment is designed to collect motor imagery EEG da-ta with four experimenters and adds brain-computer interface ( BCI ) Competition IV public motor imagery experimental data to verify the method. The data demonstrates that compared with the meth-od of selecting too many or too few data channels, the time-frequency cross mutual information meth-od using motor imagery can improve the recognition accuracy and reduce the amount of calculation.
文献关键词：
中图分类号：
作者姓名：
REN Bin;PAN Yunjie
作者机构：
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics,School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,P.R.China
文献出处：
引用格式：
[1]REN Bin;PAN Yunjie-.Research on time-frequency cross mutual of motor imagination data based on multichannel EEG signal)[J].高技术通讯（英文版）,2022(01):21-29
A类：
TFCMI,experimenters
B类：
Research,frequency,cross,mutual,motor,imagination,data,multichannel,EEG,signal,At,present,electroencephalogram,acquisition,equipment,used,collect,imagery,there,problem,selecting,multiple,channels,Choosing,too,many,will,result,large,amount,calculation,Components,irrelevant,task,interfere,required,features,which,not,conducive,real,processing,Using,few,loss,useful,information,low,robustness,method,motion,proposed,This,determines,targeted,manner,uses,common,spatial,pattern,mode,extraction,support,vector,chine,classification,An,designed,four,adds,brain,computer,interface,BCI,Competition,IV,public,experimental,verify,demonstrates,that,compared,using,can,improve,recognition,accuracy,reduce
AB值：
0.472198
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