  Human-swarm interaction has recently gained attention due to its plethora of
new applications in disaster relief, surveillance, rescue, and exploration.
However, if the task difficulty increases, the performance of the human
operator decreases, thereby decreasing the overall efficacy of the human-swarm
team. Thus, it is critical to identify the task difficulty and adaptively
allocate the task to the human operator to maintain optimal performance. In
this direction, we study the classification of task difficulty in a human-swarm
interaction experiment performing a target search mission. The human may
control platoons of unmanned aerial vehicles (UAVs) and unmanned ground
vehicles (UGVs) to search a partially observable environment during the target
search mission. The mission complexity is increased by introducing adversarial
teams that humans may only see when the environment is explored. While the
human is completing the mission, their brain activity is recorded using an
electroencephalogram (EEG), which is used to classify the task difficulty. We
have used two different approaches for classification: A feature-based approach
using coherence values as input and a deep learning-based approach using raw
EEG as input. Both approaches can classify the task difficulty well above the
chance. The results showed the importance of the occipital lobe (O1 and O2)
coherence feature with the other brain regions. Moreover, we also study
individual differences (expert vs. novice) in the classification results. The
analysis revealed that the temporal lobe in experts (T4 and T3) is predominant
for task difficulty classification compared with novices.
