Methodological advances for detecting physiological synchrony during dyadic interactions

A defining feature of many physiological systems is their synchrony and reciprocal influence. An important challenge, however, is how to measure such features. This paper presents two new approaches for identifying synchrony between the physiological signals of individuals in dyads. The approaches are adaptations of two recently-developed techniques, depending on the nature of the physiological time series. For respiration and thoracic impedance, signals that are measured continuously, we use Empirical Mode Decomposition to extract the lowfrequency components of a nonstationary signal, which carry the signal's trend. We then compute the maximum cross-correlation between the trends of two signals within consecutive overlapping time windows of fixed width throughout each of a number of experimental tasks, and identify the proportion of large values of this measure occurring during each task. For heart rate, which is output discretely, we use a structural linear model that takes into account heteroscedastic measurement error on both series. The results of this study indicate that these methods are effective in detecting synchrony between physiological measures and can be used to examine emotional coherence in dyadic interactions.