Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel
approach for controlling external devices. BCI technologies can be important enabling technologies for
people with severe mobility impairment. Endogenous paradigms, which depend on user-generated
commands and do not need external stimuli, can provide intuitive control of external devices. This
paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile
robots, and robotic arms. These technologies must be able to navigate complex environments
or execute fine motor movements. Brain control of these devices presents an intricate research
problem that merges signal processing and classification techniques with control theory. In particular,
obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder
output signals can be unstable. These issues present myriad research questions that are discussed
in this review paper. This review covers papers published until the end of 2021 that presented
BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control,
stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user
experience. The paper concludes with a discussion of open questions and avenues for future work.peer-reviewe
