Crucial rhythms and subnetworks for emotion processing extracted by an interpretable deep learning framework from EEG networks

Abstract Electroencephalogram (EEG) brain networks describe the driving and synchronous relationships among multiple brain regions and can be used to identify different emotional states. However, methods for extracting interpretable structural features from brain networks are still lacking. In the current study, a novel deep learning structure comprising both an attention mechanism and a domain adversarial strategy is proposed to extract discriminant and interpretable features from brain networks. Specifically, the attention mechanism enhances the contribution of crucial rhythms and subnetworks for emotion recognition, whereas the domain-adversarial module improves the generalization performance of our proposed model for cross-subject tasks. We validated the effectiveness of the proposed method for subject-independent emotion recognition tasks with the SJTU Emotion EEG Dataset (SEED) and the EEGs recorded in our laboratory. The experimental results showed that the proposed method can effectively improve the classification accuracy of different emotions compared with commonly used methods such as domain adversarial neural networks. On the basis of the extracted network features, we also revealed crucial rhythms and subnetwork structures for emotion processing, which are consistent with those found in previous studies. Our proposed method not only improves the classification performance of brain networks but also provides a novel tool for revealing emotion processing mechanisms.