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Objective Although the stationary subspace analysis (SSA) algorithm in EEG study makes certain achievements, the algorithm is still not perfect.Classification error of EEG data is relatively large, so it is necessary to further strengthen and optimize the algorithm and reduce the classification error.An SSA algorithm based on Jensen Shannon divergence (JSD) is proposed in this paper, and the improved algorithm is applied to the motor imagery EEG signal.Methods First, we elaborate the principle of SSA and JSD, and then use JSD to replace Kullback-Leibler divergence (KLD), and simulate the improved algorithm (as JSSA algorithm).The SSA algorithm and JSSA algorithm are applied to motor imagery EEG (data Graz2003 and Graz2008), and t test methods are applied for consideration of SSA and JSSA algorithm’s classification result which is significant to improve the accuracy.Results Compared with the common algorithm, SSA algorithm can improve the classification accuracy of the motor imagery EEG data, and the algorithm based on JSSA algorithm can make the classification results of movement imagination EEG signal more accurate than based on SSA.Conclusions Compared with the SSA algorithm, the accuracy of the JSD algorithm is better, so that the classification accuracy of motor imagery EEG is higher.