Integration of artificial intelligence and graph neural networks (GNN) for modeling human emotions based on electroencephalogram (EEG) signals

Electroencephalogram (EEG) signals are increasingly used for emotion recognition because of their non-invasive nature and high temporal resolution. However, their nonlinear and complex dynamics remain challenging to analyze. This study applies Graph Neural Networks (GNNs) to classify emotions from EEG by representing neural activity as graphs. Using the SEED-V dataset, the preprocessing included filtering, downsampling, segmentation, labelling, and feature extraction (DE, PSD, ApEn, and statistical metrics). These graph-based datasets were then evaluated with four models: GCN, GAT, ST-GCN, and DCNN. Experimental results demonstrated that GAT consistently outperformed the others, achieving validation accuracy above 97% at two to three layers while maintaining stable convergence. ST-GCN provided stronger modelling of temporal dependencies but required longer training, whereas GCN showed steady yet modest improvement, and DCNN yielded the weakest performance. These findings highlight the importance of model depth and attention mechanisms in EEG-based emotion recognition and provide practical insights for designing intelligent systems capable of interpreting human emotions more reliably. Limitations include the absence of independent test sets and efficiency analysis, which open avenues for future research on generalisation, multimodal integration, and dynamic graph learning.