  Machine learning has achieved great success in many applications, including
electroencephalogram (EEG) based brain-computer interfaces (BCIs).
Unfortunately, many machine learning models are vulnerable to adversarial
examples, which are crafted by adding deliberately designed perturbations to
the original inputs. Many adversarial attack approaches for classification
problems have been proposed, but few have considered target adversarial attacks
for regression problems. This paper proposes two such approaches. More
specifically, we consider white-box target attacks for regression problems,
where we know all information about the regression model to be attacked, and
want to design small perturbations to change the regression output by a
pre-determined amount. Experiments on two BCI regression problems verified that
both approaches are effective. Moreover, adversarial examples generated from
both approaches are also transferable, which means that we can use adversarial
examples generated from one known regression model to attack an unknown
regression model, i.e., to perform black-box attacks. To our knowledge, this is
the first study on adversarial attacks for EEG-based BCI regression problems,
which calls for more attention on the security of BCI systems.
