High-frequency SSVEP-based Navigation of a Humanoid Robot

Low- and medium-frequency steady-state visual evoked potentials（SSVEPs) can easily induce visual fatigue, and current high-frequency SSVEP paradigms place a high demand on a stimulation equipment. Therefore, we use a phase-coded method to build a user interface for a high-frequency SSVEP based brain robot interaction（BRI) on a regular liquid crystal display. Considering the difficulty in recognizing high-frequency SSVEP, we propose a fuzzy method to improve the efficiency of brain signal classification. Result of on-line humanoid robot navigation experiments show that the medium-frequency SSVEP-based brain-robot navigation system easily made subjects uncomfortable and achieved an average accuracy rate of 92.44%, a collision number of 2.14 times/trial, and an average operating frequency of 11.23 times per minute. Conversely, the application of the high-frequency paradigm to the system reduced the subjects' visual fatigue and gave better results with an average accuracy rate of 93.31%, a collision time of 1.89 times/trial and an operating frequency of 12.05 times per minute.