  Training for telerobotic systems often makes heavy use of simulated
platforms, which ensure safe operation during the learning process. Outer space
is one domain in which such a simulated training platform would be useful, as
On-Orbit Operations (O3) can be costly, inefficient, or even dangerous if not
performed properly. In this paper, we present a new telerobotic training
simulator for the Canadarm2 on the International Space Station (ISS), which is
able to modulate workload through the addition of confounding factors such as
latency, obstacles, and time pressure. In addition, multimodal physiological
data is collected from subjects as they perform a task from the simulator under
these different conditions. As most current workload measures are subjective,
we analyse objective measures from the simulator and EEG data that can provide
a reliable measure. ANOVA of task data revealed which simulator-based
performance measures could predict the presence of latency and time pressure.
Furthermore, EEG classification using a Riemannian classifier and
Leave-One-Subject-Out cross-validation showed promising classification
performance and allowed for comparison of different channel configurations and
preprocessing methods. Additionally, Riemannian distance and beta power of EEG
data were investigated as potential cross-trial and continuous workload
measures.
