High-density electrode channels are commonly used in brain-computer interface (BCI) systems to obtain high spatial resolution EEG signals, but at the same time, too many noise channels are introduced, which affect the decoding performance of electroencephalogram (EEG). In order to eliminate irrelevant noise channels, a channel selection method based on Tikhonov regularized co-spatial pattern (TRCSP) and L2 norm for motor imagery EEG is proposed this paper. Firstly, the optimal spatial filter is obtained based on TRCSP and classifier, and then the weight values of each channel obtained by the spatial filter are sorted based on L2 norm. The data of the first K channels are used for CSP feature extraction, and the optimal K value is determined according to the classification accuracy of the classifier, so as to obtain the optimal number of channels and channel combination. In the experiment, six classifiers were used on the BCI competition III (2005) dataset IVa and self-collected dataset from our laboratory to verify the effectiveness of the proposed channel selection method. The average classification accuracy of the proposed method on the two datasets reached 87.57% and 74.32%, respectively, better than other existing methods.