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.