人工知能学会

Electroencephalography (EEG)-based emotion recognition offers a noninvasive, cost-effective approach with applications in psychology, healthcare, and education. Accurate recognition of fear emotions is crucial for diagnosing and treating conditions such as phobias and anxiety disorders. This study classifies fear emotions into four levels using the DEAP dataset, leveraging Graph Neural Networks (GNNs) integrated with Long-Short-Term Memory (LSTM) networks. Two architectures, GIN-LSTM and ECLGCNN, were evaluated with raw EEG signals and Differential Entropy (DE) features. Performance was assessed using 10-fold and Leave-One-Subject-Out (LOSO) cross-validation, achieving a peak accuracy of 99.23% in 10-fold CV and 36.57% in LOSO CV, both surpassing prior studies. However, the LOSO results reveal limited generalizability to unseen subjects, highlighting the need for further research to enhance adaptability and robustness. This study demonstrates the potential of GNN-LSTM models for fear emotion classification and underscores the importance of addressing inter-subject variability to improve real-world applicability.