The brain computer interface (BCI) researchers have tried to investigate the most discriminative imagery tasks to control BCI devices. In this study, we seek to determine the two most discriminative directions of the cursor movement imagery EEG data among up/down/right/left movement imagery directions. The training and testing EEG data sets were recorded from three healthy human subjects in one week of delay. The motor imagery features were extracted using common spatial patterns (CSP), which is one of the most popular algorithms in the BCI community. Then, the features were classified by using the k-nearest neighbor (k-NN), support vector machine (SVM) and linear discriminant analysis (LDA) algorithms. The achieved results showed that imagination of up-right and down-left cursor movement imagery tasks were the two most discriminative directions among the other task pairs.