Classification of low level surface electromyogram using independent component analysis

Abstract

There is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer-assisted devices. Surface electromyogram (SEMG) is a non-invasive measure of the muscle activities but is not reliable because there are a multiple simultaneously active muscles. This study proposes the use of independent component analysis (ICA) for SEMG to separate activity from different muscles. A mitigation strategy to overcome shortcomings related to order and magnitude ambiguity related to ICA has been developed. This is achieved by using a combination of unmixing matrix obtained from FastICA analysis and weight matrix derived from training..