Evolutionary fuzzy discriminant analysis feature projection technique in myoelectric control

Abstract

The controller of a multifunction prosthetic hand usually employs a pattern recognition scheme to discriminate between the myoelectric signals (MES) from different classes of the forearm movements. The MES is recorded using a multichannel approach that makes the feature vector size very large. Hence a dimensionality reduction technique is needed to identify an informative moderate size feature set. This paper proposes a novel feature projection technique based on a combination of fisher linear discriminant analysis (LDA), fuzzy logic (FL), and differential evolution (DE) optimization technique. The new technique, DEFLDA, assigns different membership degrees to the data points in order to reduce the effect of overlapping points in the discrimination process. Furthermore, an optimized weighting scheme is presented in which certain weights are assigned to the features according to their contribution in the discrimination process. The proposed DEFLDA is tested on different datasets and compared with other projection techniques to prove its functionality.