Graph-based learning of nonlinear physiological interactions for classification of emotions

Emotion recognition has been drawing the attention of researchers and practitioners in recent years. While various research studies show successful applications to recognize and distinguish emotions based on physiological responses using machine learning techniques, less research to date is focused on the network properties of physiological interactions under different emotional states. To this end, we propose a multi-modal graph learning framework to quantify the interactions among physiological systems and present representative networks associated with emotional states. More specifically, we introduce a novel information-theoretic-based time delay stability to quantify complex interactions between physiological modalities. We test our quantification approach on three publicly available benchmark databases for emotion recognition and demonstrate the comparative performances of measuring the interactions of physiological systems in response to emotional states. Finally, we present the visualization of multi-modal physiological network topology, which may be useful for emotional interpretations in practice.