The Shared Control Method For Brain-Actuated Robotic Systems

Brain computer interface (BCI) is a new tool of human-computer interface which do not depend on the neuromuscular organization, it has a broad prospects in the field of medical rehabilitation, biomedical, entertainment, etc. Related research groups have increased to a large number, the research value of BCI has received wide attention, brain-actuated robot is one of the most important research fields. Brain-actuated robot can control the robot with brain signal based on ne BCI technology and robot technology, therefore dangerous working and assistive devices can be achieved with a new method. But due to the low accuracy rate of brain signal recognition in asynchronous BCI, robot cannot be controlled exactly. In order to solve this issue, shared control with assistant technology of robot should be introduced.Shared control enhances the performance of brain actuated-robot greatly, but the current shared control method only combines intelligent control simply, coordination control of operator and robot is not considered in aspect of control method. Furthermore, the stability of system may be influenced by accidental event and uncertain factors. In addition, the current shared control methods use discrete event system (DES) to model, the asynchronous nature of DES is suitable for system description. But the stability of system will be affected by the chattering effects which will be introduced into the system when switches between the hard boundaries of multi-states.To analyze the drawback of shared control, the method based on fuzzy discrete event system (FDES) is proposed in this paper. This method can reduce the instability of system caused by switches between hard boundaries of states and increase flexibility of system. In addition, the fuzzy supervisory method can compensate the brain control commonds. Moreover, wavelet transform and common spatial pattern (CSP) are used for feature extraction, support vector machines (SVM) and probability statistics are used for classification of asynchronous data without relax training data, the classification accuracy increase13%than champion algorithm. Finally, brain actuated robot system is composed of BCI2000and MobileSim based on algorithm of brain signal recognition and shared control. To compare with shared control of FDES and DES, subject1’average completion times enhance100%, average completion time reduce7%, average collision times reduce50%, average brain control commands reduce2%; Subject2’average completion time reduces22.3%, brain control commands reduce19.5%.Shared control based FDES also can reduce fatigue according to the results of NASA-TLX. The result of online experiment verify the effectiveness of shared control method.