Dynamical Causal Graph Neural Network for EEG Emotion Recognition

Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) emotion recognition. In this paper, we propose a novel dynamical causal graph neural network (DCGNN) based on the effective causal connectivity of brain function network for EEG emotion recognition, in which Greedy Equivalence Search (GES) is used to find the optimal causal graph topology associated with the adjacent matrix of DCGNN. To this end, we firstly construct a skeleton graph using canonical correlation analysis (CCA) and then use GES to optimize the directional graph topology of DCGNN. Then, learnable weight parameters associated with the adjacent matrix are learnt during the model training of DCGNN. Additionally, a sparse graphic constraint is employed to enhance the efficacy of emotion recognition, while a Conditional Domain Adversarial Network (CDAN) is used to integrate features with emotion labels for improving subject-independent validation. Extensive experiments and ablation studies are conducted on five public datasets, i.e., SEED, SEED-IV, SEED-V, MPED, and FACED, demonstrating that the proposed model surpasses recent causal based (Granger causality) and domain adaptation based GNN models across all experimental settings.