Deep learning approaches have been used successfully in computer vision, natural language processing and speech processing. However, the number of studies that employ deep learning on brain-computer interface (BCI) based on electroencephalography (EEG) is very limited. In this paper, we present a deep learning approach for motor imagery (MI) EEG signal classification. We perform spatial projection using common spatial pattern (CSP) for the EEG signal and then temporal projection is applied to the spatially filtered signal. The signal is next fed to a single-layer neural network for classification. We apply backpropagation (BP) algorithm to fine-tune the parameters of the approach. The effectiveness of the proposed approach has been evaluated using datasets of BCI competition III and BCI competition IV.