Multimodal Physiological Synchrony as Measure of Attentional Engagement

Abstract

When interested in monitoring attentional engagement, physiological signals can be of great value. A popular approach is to uncover the complex patterns between physiological signals and attentional engagement using supervised learning models, but it is often unclear which physiological measures can best be used in such models and collecting enough training data with a reliable ground-truth to train such model is very challenging. Rather than using physiological responses of individual participants and specific events in a trained model, one can also continuously determine the degree to which physiological measures of multiple individuals uniformly change, often referred to as physiological synchrony. As a directly proportional relation between physiological synchrony in brain activity and attentional engagement has been pointed out in the literature, no trained model is needed to link the two. I aim to create a more robust measure of attentional engagement among groups of individuals by combining electroencephalography (EEG), electrodermal activity (EDA) and heart rate into a multimodal metric of physiological synchrony. I formulate three main research questions in the current research proposal: 1) How do physiological synchrony in physiological measures from the central and peripheral nervous system relate to attentional engagement? 2) Does physiological synchrony reliably reflect shared attentional engagement in real-world use-cases? 3) How can these physiological measures be fused to obtain a multimodal metric of physiological synchrony that outperforms unimodal synchrony?