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Objective The joint approximation diagonalization (JAD) of the covariance matrix extends the common spatial pattern (CSP) algorithm to the multi-class motor imagery, in which the key feature vectors should be chosen appropriately. The most common method is to select the eigenvectors corresponding to the highest score eigenvalues. However, according to these choice criteria, the same eigenvectors are often just selected for the datasets of different classes, which may cause the failure of CSP spatial filtering and the decline of the classification accuracy. A method with the new choice criterion is proposed in this paper, which can automatically select the effective eigenvectors based on the traditional JAD algorithm.Methods The three-class motor imagery signals of two datasets (BCI Competition 2005 dataset IIIa and our own recorded experiment dataset) were used to testify the validity of the algorithm. Results The mean classification accuracies of the three-class motor imagery were calculated with the self-testing of the two datasets. The accuracies calculated by our proposed algorithm achieved 82.78% and 85.92%, which were improved by 3.44% and 3.25% respectively, compared to the traditional JAD algorithm. Conclusions This algorithm can automatically select the effective features based on CSP, and avoid selecting the useless features for classification, which can greatly improve the classification accuracies of motor imagery BCI system.