中文摘要：

针对中低频稳态视觉诱发电位（steady-state visual evoked potential,SSVEP）易于导致视觉疲劳的缺点以及现有高频范式对视觉激励载体要求较高的不足,通过应用相位编码方法,基于普通液晶显示器搭建了面向仿人机器人导航的高频SSVEP用户界面;同时针对高频SSVEP较难识别的特点,提出一种新的模糊分类方法来提高脑电信号解码效率.仿人机器人导航实验表明,基于中频SSVEP范式的脑—机器人导航系统的准确率、碰撞次数、操作频率分别为92.44%、2.14次、11.23次/min,而且极易使受试感到不适.而高频SSVEP的应用缓解了视觉疲劳,将导航系统的准确率、碰撞次数、操作频率分别提升至93.31%、1.89次与12.05次/min.

英文摘要：

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.