Abstract: In brain-computer interface (BCI) systems with a small number of training samples, a method called as regularized common special subspace decomposition (R-CSSD) algorithm was proposed to solve the problems such as low stability of the eigenvalues and poor discriminative ability of eigenvectors in electroencephalography (EEG) recognition process: In R-CSSD, regularization was introduced based on the traditional common special subspace decomposition (CSSD) algorithm. The presented method was composed of three steps : First, the training samples of the specific subject could be effectively combined with those of the other ancillary subjects by two regularization parameters; Second, a regularized special filter was built, and then the feature information of the specific subject' s EEG was extracted ; Finally, K- nearest neighbor (KNN) algorithm was used to identify motor imagery EEG. Under small-sample condition, the experimental results show that R-CSSD algorithm not only can effectively improve the stability of the eigenvalues of EEG, but also can produce high classification accuracy and less time consumption.