Fused Fuzzy Petri Nets: A Shared Control Method for Brain–Computer Interface Systems

It is hard to grasp objects based on brain–computer interface (BCI) by brain-actuated robot arm and hand due to its high degree of freedom. Shared control strategy and hybrid BCI are research trends to solve this control problem of brain-actuated discrete event system. We propose a new shared control method based on fused fuzzy Petri nets (PNs) for combining the robot automatic control (AC) and the brain-actuated control. This method takes the advantages of both fuzzy control and PNs such as easy modeling, robustness, and effectiveness. Both MATLAB simulation test and Barrett robot hand practical experiments show that the proposed method performs much better than AC or BCI control independently. In the online BCI practical experiment, the user successfully control the Barrett robot hand to grasp object avoiding obstacle in whole ten random scenes by our shared control method and hybrid BCI. Compared with BCI control, the user needs not to synchronously work according to the specific paradigm in the whole process. Meanwhile, our method improves safety and robustness by comparing with AC.