Electroencephalogram-based Emotion Recognition with Hybrid Graph Convolutional Network Model

In this rapidly changing world, machine learning has been creating a huge impact in daily aspects from smart cities to self-driving cars. One of these will be the contribution to the brain-computer interface (BCI), where brain signals are used to identify the emotions of people during various events in people's lives. In this research paper, we are proposing a multi-channel emotion recognition based on Electroencephalo-gram (EEG), using a fusion of a graph convolutional network (GCN) model and 1D Convolutional Neural Network (CNN) which classify emotions better than various existing research. Convolutional models are best known for finding features and hidden properties, and a graph convolutional network is best for connected data, which uses nodes and graphs, along with the embedded…