Swarm Intelligence based Dimensionality Reduction for Myoelectric Control

Recent approaches in multifunction myoelectrically controlled prosthetic devices revealed that dimensionality reduction plays a significant role in the overall system performance. In this paper, a new feature selection method is developed based on a mixture of particle swarm optimization (PSO) method and the concept of mutual information (MI). The new method, termed PSO-MI, is adopted as a dimensionality reduction tool for myoelectric control. The PSO-MI employs the MI measure to aid in controlling the movements of particles in the solution space, thus forming a kind of a hybrid filter-wrapper method. The new PSO-MI is able to account for the interaction property between the features in the selected subset, thus producing high classification accuracies. A dataset of transient myoelectric signal (MES) consisting of six classes of hand grasp is utilized to test the performance of the proposed method. It is proved that the PSO-MI outperforms other methods adopted for dimensionality reduction in myoelectric control achieving 95.5% of classification accuracy across six classes problem.