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

Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information