JAN-Enhanced Capsule Networks with Pre-trained Features for Cross-Subject Emotion Recognition

In EEG-based emotion recognition, traditional shallow convolutional methods often struggle with effective feature capture, particularly in cross-subject scenarios where domain alignment is crucial. This study proposes a novel approach: Utilizing a pre-trained model for rapid feature extraction, followed by the integration of Joint Maximum Mean Discrepancy(JMMD) loss within the Joint Adaptation Network(JAN) framework and dynamic routing in Capsule Networks, forming the dynamic domain adaptation model DA-PCN. The pre-trained VGG16 enhances the capture of complex emotional patterns and improves generalization across subjects. These features are further refined through a dual-layer Capsule Network, which aligns them across different domains at each layer. Assessments across various subjects and multiple videos from the same subjects using the DEAP dataset support the effectiveness of this method.