Quantifying EDA synchrony through joint sparse representation: A case-study of couples' interactions

The co-variation degree between individuals in their physiological signals can reveal insights about the quality of their interaction as well as their personal characteristics. In an effort to capture the amount of synchrony between Electrodermal Activity (EDA) streams occurring in parallel during dyadic interactions, we propose Sparse EDA Synchrony Measure (SESM), an index derived from the joint sparse representation of EDA ensembles. Sparse decomposition is performed using Simultaneous Orthogonal Matching Pursuit (SOMP) from a knowledge-driven dictionary of tonic and phasic atoms, capturing the slow-varying trends and high-frequency signal fluctuations, respectively. At each iteration the atom having the maximum average correlation with the residuals is selected. We compute SESM as the negative natural logarithm of the joint reconstruction error and evaluate it with data from interactions of married and young dating couples participating in tasks of varying emotional intensity. Through statistical analysis and multiple linear regression experiments, our results indicate that SESM depicts significant differences across tasks in both datasets considered and can be associated to individuals' attachment-related characteristics.