  Physiological computing uses human physiological data as system inputs in
real time. It includes, or significantly overlaps with, brain-computer
interfaces, affective computing, adaptive automation, health informatics, and
physiological signal based biometrics. Physiological computing increases the
communication bandwidth from the user to the computer, but is also subject to
various types of adversarial attacks, in which the attacker deliberately
manipulates the training and/or test examples to hijack the machine learning
algorithm output, leading to possible user confusion, frustration, injury, or
even death. However, the vulnerability of physiological computing systems has
not been paid enough attention to, and there does not exist a comprehensive
review on adversarial attacks to them. This paper fills this gap, by providing
a systematic review on the main research areas of physiological computing,
different types of adversarial attacks and their applications to physiological
computing, and the corresponding defense strategies. We hope this review will
attract more research interests on the vulnerability of physiological computing
systems, and more importantly, defense strategies to make them more secure.
