In this study, we aimed to use the motor-imagery electroencephalography (EEG) signal to construct a Brain Computer Interface (BCI) system. We developed an EEG-based real-time cursor control system on LabVIEW platform. EEG signals of left or right motor imagery on C3, and C4 channels were collected using OpenBCI amplifier system. The experimental protocol consisted of a training stage and a self-controlled stage with several runs in each stage. In the training stage, EEG signals were collected while subjects were asked to look at the moving cursor with a constant speed toward left/right, and pretended the cursor was controlled by their imagination. In the self-controlled stage, the movement of the cursor was controlled based on the concordance between the classification results and preset direction. Ten subjects were enrolled in this study. We found that the classification rate was associated with the consistency of the individual ERD/ERS frequency bands across different runs. If a subject presented a stable frequency band for the motor imaginary, the classification rate of the developed BCI system can reach a satisfactory performance with the highest classification rate of 93%. In conclusion, our results showed that the efficacy of our BCI system highly relied on the stability of the individual frequency pattern.