Motor imagery (MI)-based brain-computer interface (BCI) allows users to control external devices using the brain signal patterns induced by the imagination of movements. Since these patterns have high variability between subjects and sessions, the BCI system necessarily requires 20-30 minutes for the calibration process each time the system is used. This time-consuming process requires a high level of the user's concentration; most users experience uncomfortable feelings such as tiredness, exhaustion, and loss of attention, which are symptoms of mental fatigue. In this paper, we introduce a self-paced training that terminates the calibration process within a few minutes. In this training paradigm, users perform MI tasks continuously without an inter-stimulus-interval (ISI). Also, we propose a data selection method to extract the most prominent features from the short calibration data by assuming the data distribution probabilistically and using the prior knowledge of event-related desynchronization (ERD) patterns. The results from 19 subjects indicate that the proposed method gained a comparable classification performance to the conventional method but with a much shorter calibration period (12 min/73.8%, 30 min/76.1%, respectively). In this regard, the proposed method could be of great benefit for real-world BCI applications by providing a quicker calibration process.