  Objective. We investigated the neural correlates of workload buildup in a
fine visuomotor task called the boundary avoidance task (BAT). The BAT has been
known to induce naturally occurring failures of human-machine coupling in high
performance aircraft that can potentially lead to a crash; these failures are
termed pilot induced oscillations (PIOs). Approach. We recorded EEG and
pupillometry data from human subjects engaged in a flight BAT simulated within
a virtual 3D environment. Main results. We find that workload buildup in a BAT
can be successfully decoded from oscillatory features in the
electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma
spectral bands of the EEG all contribute to successful decoding, however gamma
band activity with a lateralized somatosensory topography has the highest
contribution, while theta band activity with a frontocentral topography has the
most robust contribution in terms of real world usability. We show that the
output of the spectral decoder can be used to predict PIO susceptibility. We
also find that workload buildup in the task induces pupil dilation, the
magnitude of which is significantly correlated with the magnitude of the
decoded EEG signals. These results suggest that PIOs may result from the
dysregulation of cortical networks such as the locus coeruleus (LC) anterior
cingulate cortex (ACC) circuit. Significance. Our findings may generalize to
similar control failures in other cases of tight man machine coupling where
gains and latencies in the control system must be inferred and compensated for
by the human operators. A closed-loop intervention using neurophysiological
decoding of workload buildup that targets the LC ACC circuit may positively
impact operator performance in such situations.
