EOG controlled mobile robot using Radial Basis Function Networks

Controlling a mobile robot using human biopotential signals has been a common problem in the field of assistive robotics. Not only it is enough to analyze the biosignal characteristics and interpret motion commands from the raw signal, but also an efficient learning algorithm may help to overcome varying characteristics of the biosignal for the sake of robust control of the mobile robot. In this work, an efficient learning algorithm utilizing Radial Basis Function Networks have been studied and applied to EOG signals in order to control a mobile robot. Obtained results show that RBF network is successful in learning the biosignal characteristics and producing sufficient control signals to control a mobile robot.