Time-resolved functional connectivity from high-density EEG for characterizing the level of consciousness in behaviorally unresponsive patients

Brain-computer interfaces (BCIs) have shown enormous promise in the detection of consciousness in minimally responsive individuals. To date, most BCIs have relied on the presence of high-level cognitive abilities (e.g. attention, language comprehension) in non-responsive individuals, resulting in a large number of cases of undetected - or covert - consciousness. An alternate approach is to measure the underlying properties of brain networks, which makes no assumptions about the presence of certain cognitive capacities. Brain networks can be represented through functional connectivity of different brain areas. To date, the vast majority of studies have used time-averaged functional connectivity to represent a state of consciousness. In this paper, we compare time-averaged versus time-resolved functional connectivity, and the information contained by each in different states of consciousness. We present a novel analysis to evaluate the dynamic properties of time-resolved, high-resolution estimates of phase-based functional connectivity using weighted phase lag index (wPLI) calculated from high-density EEG. In a case study of two individuals in disorders of consciousness, we demonstrate that time-resolved functional connectivity reflects the dynamic properties of brain networks, providing more information about an individual's state of consciousness than traditional time-averaged approaches. Our findings support time-resolved functional connectivity as the basis for a passive BCI with the potential to characterize the level of consciousness in behaviourally unresponsive patients.