A Domain Generative Graph Network for EEG-Based Emotion Recognition

Emotion is a human attitude experience and corresponding behavioral response to objective things. Effective emotion recognition is important for the intelligence and humanization of brain-computer interface (BCI). Although deep learning has been widely used in emotion recognition in recent years, emotion recognition based on electroencephalography (EEG) is still a challenging task in practical applications. Herein, we proposed a novel hybrid model that employs generative adversarial networks to generate potential representations of EEG signals while combining graph convolutional neural networks and long short-term memory networks to recognize emotions from EEG signals. Experimental results on DEAP and SEED datasets show that the proposed model achieved the promising emotion classification performance compared with the state-of-the-art methods.

Automated summary

Emotion is a human attitude experience and corresponding behavioral response to objective things. Effective emotion recognition plays an important role in the intelligence and humanization of brain-computer interface (BCI). Although deep learning has been widely used for emotion recognition, it remains challenging in the practical application of EEG-based emotion recognition. In this study, a novel hybrid model combining generative adversarial networks, graph convolutional neural networks, and long short-term memory networks was proposed to recognize emotions from EEG signals. Experimental results on DEAP and SEED datasets demonstrated that the proposed model achieved promising emotion classification performance compared with state-of-the-art methods.