This is a post about an article that was first-authored by my PhD student Ira, for a special edition on EEG methods coming out soon in Developmental Cognitive Neuroscience. You can read the whole paper here.
Most of the early papers that have come out in interpersonal connectivity research have basically used the same approach. You measure a load of free interaction data, and then you compute your interpersonal connectivity measure – whether it’s Granger Causality, or Phase Locking Value, or Wavelet Transform Coherence – in a way which basically collapses the time dimension out of it. For example, some papers look at how connectivity differs on average between different dyads, and others look at how connectivity differs between conditions (e.g. between direct and averted gaze). But we don’t have the methods yet to look at how connectivity changes over time – for example, relative to particular behavioural events.
This is a real pain because it makes it much harder to work out how, exactly, connectivity between two interacting brains is established and maintained. Take, for example, the finding that in the 3-9Hz range, neural activity in one partner consistently predicts the other partner’s neural activity more strongly during direct compared with indirect gaze. How exactly is this possible? How can two brains influence one other over such a fine-grained scale?
Well, there are a bunch of different possibilities, and to tell them apart it would certainly help to be able to look at event-related patterns of change: ie how does connectivity change relative to particular, pre-specified events. And in the paper we basically lay out some algorithms that would let people do this. We look at concurrent entrainment (e.g. power correlations, phase locking) and sequential entrainment (e.g. Granger causality). And we apply them to three aspects of the brain signal – namely amplitude, power and phase.
Anyway, it’s pretty exciting!! If you’re interested you can read the whole paper here.
