  Brain-controlled vehicle (BCV) is an already established technology usually
designed for disabled patients. This review focuses on the most relevant topics
on the brain controlling vehicles, especially considering terrestrial BCV
(e.g., mobile car, car simulators, real car, graphical and gaming cars) and
aerial BCV, also named BCAV (e.g., real quadcopter, drone, fixed wings,
graphical helicopter and aircraft) controlled using bio-signals such as
electroencephalogram (EEG), electrooculogram and electromyogram. For instance,
EEG-based algorithms detect patterns from motor imaginary cortex area of the
brain for intention detection, patterns like event related
desynchronization/event related synchronization, state visually evoked
potentials, P300, and generated local evoked potential patterns. We have
identified that the reported best performing approaches employ machine learning
and artificial intelligence optimization methods, namely support vector
machine, neural network, linear discriminant analysis, k-nearest neighbor,
k-means, water drop optimization and chaotic tug of war optimization
optimization. We considered the following metrics to analyze the efficiency of
the different methods: type and combination of bio-signals, time response, and
accuracy values with the statistical analysis. The present work provides an
extensive literature review of the key findings of previous ten years,
indicating the future perspectives in the field.
