Asynchronous Brain Machine Interface System for Autonomous Assembly Robot Based on Facial Expression Paradigm

Brain-Machine Interfaces (BMI) establish a new information communication and control channel between the brain and peripheral devices, which independent of the spinal cord and peripheral nervous system. In recent years, it has become a hotspot research in the field of international intelligent science. However, the current BMI usually belong to synchronous system, which requires subjects to perform specific tasks at specific program setting, which greatly limits the development of BMI system. The development of asynchronous BMI has attracted extensive attention of researchers, whose study focus on the detection of idle state. Hence, this paper proposed a novel asynchronous BMI detection method for idle and working state recognition, which is based on Hurst exponent and Support Vector Machine (SVM). In this work, an asynchronous BMI system based on facial expression paradigm was constructed to control the assembly robot. The method of Hurst exponent and SVM was used to recognize the asynchronous state and different facial expressions. Then, offline and online experiments were constructed to verify the effective of proposed method. The experimental results demonstrated that there is a large difference in the Hurst exponent between different facial expressions state and the idle state, and the average accuracy of SVM classification results can reach more than 90%.