  Developments in Brain Computer Interfaces (BCIs) are empowering those with
severe physical afflictions through their use in assistive systems. Common
methods of achieving this is via Motor Imagery (MI), which maps brain signals
to code for certain commands. Electroencephalogram (EEG) is preferred for
recording brain signal data on account of it being non-invasive. Despite their
potential utility, MI-BCI systems are yet confined to research labs. A major
cause for this is lack of robustness of such systems. As hypothesized by two
teams during Cybathlon 2016, a particular source of the system's vulnerability
is the sharp change in the subject's state of emotional arousal. This work aims
towards making MI-BCI systems resilient to such emotional perturbations. To do
so, subjects are exposed to high and low arousal-inducing virtual reality (VR)
environments before recording EEG data. The advent of COVID-19 compelled us to
modify our methodology. Instead of training machine learning algorithms to
classify emotional arousal, we opt for classifying subjects that serve as proxy
for each state. Additionally, MI models are trained for each subject instead of
each arousal state. As training subjects to use MI-BCI can be an arduous and
time-consuming process, reducing this variability and increasing robustness can
considerably accelerate the acceptance and adoption of assistive technologies
powered by BCI.
