Development of a P300 brain-machine interface and design of an elastic mechanism for a rehabilitation robot

Abstract

This paper focuses on the development of a P300 speller and the design of a rehabilitation robot using a brain-machine interface. The combined feature set provides a norm that can be used to assess trends of the user's increased or decreased independence. The combined feature set is found to maintain a 90% sorting rate; it can also reduce the relationship of individual independence for each subject. Among the results, the highest P300 classification accuracy can be increased by 36.04%. A novel adaptive coupled elastic actuator (ACEA) is proposed that uses adjustable characteristics to adapt to the applied output and input forces, thus ensuring safe human-machine interaction without the use of complex control strategies. The proposed robotic system uses variable impedance to achieve adaptability and safety in dynamic unstructured environments. This paper discusses the design, model, control, and performance of the ACEA.