  Machine learning models are known to be vulnerable to adversarial
perturbations in the input domain, causing incorrect predictions. Inspired by
this phenomenon, we explore the feasibility of manipulating EEG-based Motor
Imagery (MI) Brain Computer Interfaces (BCIs) via perturbations in sensory
stimuli. Similar to adversarial examples, these \emph{adversarial stimuli} aim
to exploit the limitations of the integrated brain-sensor-processing components
of the BCI system in handling shifts in participants' response to changes in
sensory stimuli. This paper proposes adversarial stimuli as an attack vector
against BCIs, and reports the findings of preliminary experiments on the impact
of visual adversarial stimuli on the integrity of EEG-based MI BCIs. Our
findings suggest that minor adversarial stimuli can significantly deteriorate
the performance of MI BCIs across all participants (p=0.0003). Additionally,
our results indicate that such attacks are more effective in conditions with
induced stress.
