  Machine learning has achieved great success in electroencephalogram (EEG)
based brain-computer interfaces (BCIs). Most existing BCI research focused on
improving its accuracy, but few had considered its security. Recent studies,
however, have shown that EEG-based BCIs are vulnerable to adversarial attacks,
where small perturbations added to the input can cause misclassification.
Detection of adversarial examples is crucial to both the understanding of this
phenomenon and the defense. This paper, for the first time, explores
adversarial detection in EEG-based BCIs. Experiments on two EEG datasets using
three convolutional neural networks were performed to verify the performances
of multiple detection approaches. We showed that both white-box and black-box
attacks can be detected, and the former are easier to detect.
