With deep learning emerging as a powerful machine learning tool to build Brain Computer Interface (BCI) systems, researchers are investigating the use of different type of networks architectures and representations of brain activity to attain superior classification accuracy compared to state-of-the-art machine learning approaches, that rely on processed signal and optimally extracted features. This paper presents a deep learning driven electroencephalography (EEG)-BCI system to perform decoding of hand motor imagery using deep convolution neural network architecture, with spectrally localized time-domain representation of multi-channel EEG as input. A significant increase in decoding performance in terms of accuracy of +6.47% is obtained compared to a wideband EEG representation. We further illustrate the movement class specific feature patterns for both the architectures and demonstrate that higher difference between classes is observed using the proposed architecture. We conclude that the network trained by taking into account the dynamic spatial interactions in distinct frequency bands of EEG, can offer better decoding performance and aid in better interpretation of learned features.