Comparative of Swarm Intelligence based Wrappers for sEMG Signals Feature Selection

This paper proposes a comparative of binary swarm optimization based wrappers for ElectroMyography (EMG) feature selection. Time-domain and frequency-domain features are extracted from two EMG channels to evaluate the effect of each of them according to the accuracy and computational costs. Six binary algorithms are used in this study namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Salp Swarm Algorithm (SSA), Bat Algorithm (BA), and Particle Swarm Optimization (PSO) in the domain of machine learning for feature selection and classification. Results prove that time-domain features are enough to give satisfying classification accuracy, WOA is giving the best average classification accuracy of 80.15% but needs more execution time. Compared with others, SSA is the best algorithm according to the number of selected features, execution time, and fitness function 78.25% as accuracy.