  Characterizing the dynamic interactive patterns of complex systems helps gain
in-depth understanding of how components interrelate with each other while
performing certain functions as a whole. In this study, we present a novel
multimodal data fusion approach to construct a complex network, which models
the interactions of biological subsystems in the human body under emotional
states through physiological responses. Joint recurrence plot and temporal
network metrics are employed to integrate the multimodal information at the
signal level. A benchmark public dataset of is used for evaluating our model.
