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.