Generative Adversarial Networks (GAN) have led to important advancements in generation of time-series data in areas like speech processing. This ability of GANs can be very useful for Brain-Computer Interfaces (BCIs) where collecting large number of samples can be expensive and time-consuming. To address this issue, this paper presents a new approach for generating artificial electroencephalography (EEG) data for motor imagery. GANs here use a generator and discriminator networks that consist of Bidirectional Long Short Term Memory neurons. Trained models are evaluated using the dataset 2b from the BCI competition IV. The dataset consists of trials with left and right hand motor imagery. Separate GANs are trained to generate artificial EEG samples corresponding to the two types of trials present in the data set. For the purpose of evaluation, the time-frequency characteristics of the real and artificial EEG signals are compared using Short-Term Fourier Transform and Welch's power spectral density. The results indicate that GANs can capture important characteristics of motor imagery EEG data such as power variations in the beta-band. The power variation in the artificial generated and original signal was in the similar frequency bin when looked at Welch's power spectral density.