Operability Improvement for Myoelectric Prosthetic Hand Using Brain Machine Interface Identification of Eight Forearm Motions Based on Myoelectric Potential

This paper proposes a myoelectric prosthetic hand control system using a brain machine interface that uses electromyogram signals and other biosignals such electro-encephalographic signals. The proposed system consists of a feature vector extractor, a neural network estimator, a reference trajectory generator, and an adaptive corrector. As a preparation, an experimental system has been prototyped without adaptive correction using other biosignals. The system is to classify eight forearm motions by using two electromyogram signals and to generate reference trajectories for servo controllers of a prosthetic hand. The experimental results showed that the proposed classification algorithm has basically worked well and that the reference trajectory generator has a potential to make appropriate smooth trajectories. Furthermore, electroencephalogram signals during forearm motion identification were measured and analyzed to check whether reaction to indicate any malfunctions was observed.