Controlling a robot with a brain-computer interface based on steady state visual evoked potentials

In this paper a brain-computer interface (BCI) is presented which uses steady-state visual evoked potentials for controlling a robot. EEG is derived from three subjects to test the performance of the system. For feature extraction and classification on one hand the Minimum Energy method, and on the other hand the Fast Fourier Transformation (FFT) with linear discriminant analysis (LDA) is used. As final step a novel method was implemented which analyzes the change rate and the majority weight of redundant classifiers to improve the robustness and to provide a zero classification. The implementation is tested with a robot which is able to move forward, backward, to the left and to the right. High accuracy is achieved for all the commands. Of special interest is, that a zero-class recognition was implemented successfully which causes the robot to stop with high reliability if the subject does not look at one of the stimulation LEDs.