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Motor imagery EEG signals requires a high cost for recording, and there is a large difference for individual’s signals. Cross-subject motor imagery EEG signals recognition task belongs to a typical cross-domain learning problem with a small samples set. To solve this problem, this paper proposed a cross-domain feature learning method for motor imagery EEG signals to improve the recognition performance. The proposed method first selected the optimal measurement to align the covariance of EEG signals, and then extracted common spatial patterns（CSP） from the aligned EEG trials. Second, based on the CSP features, an optimal domain adaptation algorithm was selected to learn the optimal cross-domain features for the target domain. To validate the feasibility and effectiveness of the learned cross-domain features, a classical model was adopted to recognize the learned cross-domain features, and the comparative experiments were conducted on two public datasets. Experimental results show that the learned cross-domain features are obviously better than state-of-the-arts methods in recognition performance. In addition, this paper also compared the parameters setting, performance and efficiency for the proposed method. [ABSTRACT FROM AUTHOR]