Target Select Technology In Dynamic Environment With Brain-computer Interface(BCI)

Mind control is always one of the human’s dreams. Brain-computer interface(BCI)or Brain-Machine Interface(BMI), which could get the computer’s command from human’s brain activity signals directly without the participation of the traditional muscle nerve pathways, is regarded as a potential approach for the dream. Nowadays, BCI began to be applied effectively to rehabilitation aspects, and also has a significant research value in other sector, for instance, the industrial production.This paper used the Steady-State Visual Evoked Potential(SSVEP) potential as the raw brain signal, and decoded it to computer commands to control a robot in dynamic environment. Therefore, a direct control interface was established between human brain and external devices. Comparing with the other related BCI researches, this paper promoted the BCI system’s practicability in the true dynamic scene; by designing a new two-layer division interface. The proposed interface superimposed the stimulation on the scene, to increase the number of optional targets and precise the system’s accuracy.This paper also compared the effects of different stimulus types on the performance of the system, put forward the inverted type stimulus and stimulates source energy conjecture, and tried to make efforts to improve the universal algorithm. Our BCI system based on SSVEP was tested on the robot control experiments. An optimized synchronous control paradigm was constructed by improving the control process based on the above interface. In the experiment, subjects could control the robot to complete walking and grasp tasks. Furthermore, an asynchronous control paradigm was also designed, and its performance was discussed on offline data.The experiments results prove that the BCI system established in this paper could complete complex robot control tasks effectively in dynamic environment, when using appropriate stimulus type in its basic paradigm. The robot control synchronous paradigm can complete the preset walking and grasp task and get a good performance.