Individual hand movement detection and classification using peripheral nerve signals

This paper investigates whether the movement intent of an amputee can be detected and classified in real-time as the individual moved his/her phantom hand. We present a method to detect movement intent using neural signals from the peripheral nervous system (PNS). In addition, we classify eight types of individual hand movements using 300 ms signal segments beginning with our detected starting time. Classification is performed by applying linear discriminant analysis (LDA) on different kind of features. We compared the classification results using segments started with the detected starting time and the starting time of the command given to a subject as neural signals were recorded. The average accuracies were 73.5% in the former case and 59.4% in the latter.