人工知能学会

Graph neural networks, deep learning models designed for non-Euclidean data, have garnered attention in EEG-based emotion recognition. Recent studies explore EEG-based models and investigate multimodal models that incorporate peripheral physiological signals, such as electrooculography and electrocardiography, with ongoing research focused on feature fusion methods. The graphs used in GNNs for emotion recognition are generally constructed based on the spatial distance or the functional connectivity between channels; however, most models rely on only one type. This paper validates the effectiveness of a model that utilizes features from heterogeneous graphs and investigates various fusion methods inspired by multimodal approaches. As a result, the highest accuracy achieved was 93.87%, approximately 2% higher than that obtained using a single graph and comparable to existing methods. Furthermore, when synthesizing heterogeneous graphs, a technique that uses the embedding vector of the entire graph has proven to be more effective than one that considers individual channels.