A hybrid graph attention network with multi-dimensional features for enhanced EEG-based emotion recognition

Emotion recognition using electroencephalogram (EEG) signals is a growing focus in affective computing due to its wide-ranging applications in human-computer interaction. However, many existing studies process EEG signals as independent one-dimensional time series, overlooking their multidimensional structure and dynamic segment relationships. To address this, we propose a novel Hybrid Graph Attention Network (H-GAT) model that integrates Graph Attention Networks (GAT), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. Our model effectively captures multi-dimensional dependencies in EEG data by modeling functional relationships between EEG segments, extracting local temporal patterns, and learning temporal dynamics. The EEG signals are transformed into a graph representation, where the adjacency matrix is generated from dynamic similarity measures computed by evaluating Pearson correlations between segment-wise feature vectors. This enables the GAT module to effectively model the functional relationships across EEG segments. Additionally, a CNN layer extracts local temporal patterns, while an LSTM captures the overall temporal dynamics. These features are then fused and passed through a fully connected layer for classification. Extensive experiments conducted on the SEED and DEAP datasets demonstrate the superiority of our model, achieving an average accuracy of 97.89 ± 0.50% for binary, 96.67 ± 0.72% for ternary, on SEED data, and 93.09 ± 1.02% for valence and 93.93 ± 1.14% for arousal on the DEAP dataset. Further, cross-dataset validation experiments confirm the model's strong generalization ability across heterogeneous EEG datasets. These results not only highlight the power of hybrid neural architectures but also demonstrate the model's transformative potential to progress emotion recognition, making it a robust and highly interpretable solution in the rapidly advancing field of affective computing.