Evaluating different combinations of feature selection algorithms and cost functions applied to iPCA tuning in myoelectric control systems

A myoelectric control system extracts information from electromyographic (EMG) signals and uses it to control different types of prostheses, so that people who suffered traumatisms, paralysis or amputations can use them to execute common movements. Recent research shows that the addition of a tuning stage, using the individual component analysis (iPCA), results in improved classification performance. We propose and evaluate a set of novel configurations for the iPCA tuning, based on a biologically inspired optimization procedure, the artificial bee colony algorithm. This procedure is implemented and tested using two different cost functions, the traditional classification error and the proposed correlation factor, which involves lower computational effort. We compare the tuned system´s performance, in terms of correct classifications, to that of a system tuned using two standard algorithms, the sequential forward selection and the sequential floating forward selection. The statistical analyses of the results don´t find a significant difference among the classification performances associated with the search algorithms (p <; 0.01). On the other hand, they establish a significant difference among the classification performances related to the cost functions (p <; 0.02).