Motor imagery (MI) based Brain-computer interface (BCI) is a promising BCI paradigm that can help neuromuscular injury patients to recover or replace their motor abilities. However, electroencephalography (EEG) based MI-BCI suffers from its long calibration time and low classification accuracy, which restrict its application. Thus, it is important to reduce the calibration time of MI-BCI and enhance its prediction accuracy. In this study, we propose a filter bank Wasserstein adversarial domain adaptation framework (FBWADA) that uses a short amount of training data from a new target subject, and all collected data from an existing subject. A Convolutional Neural Networks (CNN) based feature extractor is designed to extract feature from EEG data. Filter bank strategy is employed to extract feature from multiple sub bands and integrate predictions from all sub bands. Wasserstein Generative Adversarial Networks (WGAN) based domain adaptation network aligns the marginal and conditional distribution of target and source. We evaluate our method on Data set 2a of BCI competition IV. Experiment results show that our method achieves the best performance among compared methods under different amount of training data. Performance of our method trained with certain blocks of data is similar to or better than the best comparing method trained with one more block. This indicates that our method could reduce the need for training data for at least one block.