Brain computer interface is a developing technology that can provide enhanced quality of life to individuals suffering from various disabilities. In this work, a new binary electroencephalography (EEG) signal decoding algorithm is proposed using a modified common spatial pattern and capsule network. The proposed method is realized by extracting the spectral-temporal common spatial pattern features from the EEG signals while preserving the time resolution of the signal. The resulting features are fed into the capsule network for automatic feature extraction and classification. The capsule network is known to be superior to convolutional neural networks in requiring less training data, which makes it a promising candidate for EEG signals classification. The performance of the proposed method is evaluated and compared to that of the other methods by conducting several experiments. The results demonstrate that the proposed method provides recognition accuracy higher than that provided by other methods.