In order to enhance the usability of a motor imagery-based brain-computer interface (BCI), it is highly desirable to reduce the calibration time. Due to inter-subject variability, typically a new subject has to undergo a 20-30 minutes calibration session to collect sufficient data for training a BCI model based on his/her brain patterns. This paper proposes a new subject-to-subject adaptation algorithm to reliably reduce the calibration time of a new subject to only 3-4 minutes. To reduce the calibration time, unlike several past studies, the proposed algorithm does not require a large pool of historic sessions. In the proposed algorithm, using only a few trials from the new subject, first, the new subject's data is adapted to each available historic session separately. This is done by a linear transformation minimizing the distribution difference between the two groups of EEG data. Thereafter, among the available historic sessions, the one matched the most to the new subject's adapted data is selected as the calibration session. Consequently, the previously trained model based on the selected historic session is entirely used for the classification of the new subject's data after adaptation. The proposed algorithm is evaluated on a publicly available dataset with 9 subjects. For each subject, the calibration session is selected only from the calibration sessions of the eight other subjects. The experimental results showed that our proposed algorithm not only reduced the calibration time by 85%, but also performed on average only 1.7% less accurate than the subject-dependent calibration results.