  Emotion recognition based on electroencephalography (EEG) has received
attention as a way to implement human-centric services. However, there is still
much room for improvement, particularly in terms of the recognition accuracy.
In this paper, we propose a novel deep learning approach using convolutional
neural networks (CNNs) for EEG-based emotion recognition. In particular, we
employ brain connectivity features that have not been used with deep learning
models in previous studies, which can account for synchronous activations of
different brain regions. In addition, we develop a method to effectively
capture asymmetric brain activity patterns that are important for emotion
recognition. Experimental results confirm the effectiveness of our approach.
