Research on time-frequency cross mutual of motor imagination data based on multichannel EEG signal

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.