Current approaches typically measure the connectivity between interacting physiological systems as a time-invariant property. This approach obscures crucial information about how connectivity between interacting systems is established and maintained. Here, we describe methods, and present computational algorithms, that will allow researchers to address this deficit. We focus on how two different approaches to measuring connectivity, namely concurrent (e.g., power correlations, phase locking) and sequential (e.g., Granger causality), can be applied to three aspects of the brain signal, namely amplitude, power, and phase. We guide the reader through worked examples using mainly simulated data on how to leverage these methods to measure changes in interbrain connectivity between adults and children/infants relative to events identified within continuous EEG data during a free-flowing naturalistic interaction. For each, we aim to provide a detailed explanation of the interpretation of the analysis and how they can be usefully used when studying early social interactions.

No abstract