Advancing EEG-Based Emotion Recognition: Unleashing the Power of Graph Neural Networks for Dynamic and Topology-Aware Models

In recent years, numerous studies on emotion recognition have employed non-invasive electroencephalography (EEG) to measure neuronal activity. Various Machine Learning and Deep Learning techniques have been applied to classify emotions based on EEG signals. However, Graph Neural Networks (GNNs) remain relatively unexplored in this domain. Given that GNNs can accommodate dynamic and variablesized data as input, we hypothesize their potential for superior performance compared to traditional models like Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs), which rigidly fix the distance among electrodes in a pixel-like matrix. Furthermore, GNNs are designed to leverage the biological topology between different brain regions, capturing both local and global relationships among EEG channels. In this study, two GNN models are experimented with: a Dynamical Graph Convolutional Neural Network (DGCNN) and a Regularized Graph Neural Network (RGNN). Results indicate that the DGCNN model proposed in this work outperforms state-of-the-art models and the RGNN, achieving an average accuracy of 95%, compared to 53% for the latter. Additionally, the DGCNN exhibits significantly faster training times, opening up new avenues for research in this field.