A Brain-Computer Interface Based on Steady State Visual Evoked Potentials for Controlling a Robot

In this paper a brain computer interface (BCI) based on steady state visual evoked potentials (SSVEP) is presented. For stimulation a box equipped with LEDs (for forward, backward, left and right commands) is used that flicker with different frequencies (10, 11, 12, 13 Hz) to induce the SSVEPs. Eight channels of EEG were derived mostly over visual cortex for the experiment with 3 subjects. To calculate features and to classify the EEG data Minimum Energy and Fast Fourier Transformation with linear discriminant analysis was used. Finally the change rate (fluctuation of the classification result) and the majority weight were calculated to increase the robustness and to provide a null classification. As feedback a tiny robot was used that moved forward, backward, to the left and to the right and stopped the movement if the subject did not look at the stimulation LEDs.