A Network Approach for Evaluating Coherence in Multivariate Systems: an Application to Psychophysiological Emotion Data

We present an approach for evaluating coherence in multivariate systems that considers all the variables simultaneously. We operationalize the multivariate system as a network and define coherence as the efficiency with which a signal is transmitted throughout the network. We illustrate this approach with time series data from 15 psychophysiological signals representing individuals’ moment-by-moment emotional reactions to emotional films. First, we summarize the time series through nonparametric Receiver Operating Characteristic (ROC) curves. Second, we use Spearman rank correlations to calculate relationships between each pair of variables. Third, based on the obtained associations, we construct a network using the variables as nodes. Finally, we examine signal transmission through all the nodes in the network. Our results indicate that the network consisting of the 15 psychophysiological signals has a small-world structure, with three clusters of variables and strong within-cluster connections. This structure supports an effective signal transmission across the entire network. When compared across experimental conditions, our results indicate that coherence is relatively stronger for intense emotional stimuli than for neutral stimuli. These findings are discussed in relation to multivariate methods and emotion theories.