A BRAIN-ACTUATED ROBOT CONTROLLER FOR INTUITIVE AND RELIABLE MANOEUVRING

During this master-thesis a robot controller designed for low-throughput and noisy EEG-data of a Brain Computer Interface (BCI) is implemented. The hypothesis of this master-thesis state that it is possible to design a modular and platform independent BCI-based controller for a mobile robot, which regulates the autonomy of the robot as a function of the user’s will to control. The BCI design is thoroughly described, including both the design choices regarding used brain activity signals and the pre- and post-processing of EEG data. The robot controller is experimentally tested by completing a set of missions in a simulated environment. Both quantitative and qualitative data is derived from the experimental test setup and used to evaluate the controller performance with different levels of induced noise. Additional to the robot control performance result, an offline validation of the BCI performance is depicted. Strength and weaknesses of the system design is presented based on the acquired result, and suggested solutions to improve the over-all performance is given. The produced result show that using the developed controller is a feasible approach for reliable and intuitive manoeuvring of a telepresence robot