  Humans can fluidly adapt their interest in complex environments in ways that
machines cannot. Here, we lay the groundwork for a real-world system that
passively monitors and merges neural correlates of visual interest across team
members via Collaborative Brain Computer Interface (cBCI). When group interest
is detected and co-registered in time and space, it can be used to model the
task relevance of items in a dynamic, natural environment. Previous work in
cBCIs focuses on static stimuli, stimulus- or response- locked analyses, and
often within-subject and experiment model training. The contributions of this
work are twofold. First, we test the utility of cBCI on a scenario that more
closely resembles natural conditions, where subjects visually scanned a video
for target items in a virtual environment. Second, we use an
experiment-agnostic deep learning model to account for the real-world use case
where no training set exists that exactly matches the end-users task and
circumstances. With our approach we show improved performance as the number of
subjects in the cBCI ensemble grows, and the potential to reconstruct
ground-truth target occurrence in an otherwise noisy and complex environment.
