One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using the limited number of train data may not yield an accurate BCI model. To address this problem, this paper proposes a novel subject-to-subject transfer learning framework that improves the classification accuracy using limited training data. The proposed framework consists of two steps: The first step identifies if the target subject will benefit from transfer learning using cross-validation on the few available subject-specific training data. If transfer learning is a required novel algorithm for measuring similarity, called the Jensen-Shannon ratio (JSR) compares the data of the target subject with the data sets from previous subjects. Subsequently, the previously calibrated BCI subject model with the highest similarity to the target subject is used as the BCI target model. Our experimental results using the proposed framework obtained an average accuracy of 77% using 40 subject-specific trials, outperforming the subject-specific BCI model by 3%.