Current research on neuro-prosthetics is aimed at designing several computational models and techniques to trigger the neuro-motor rehabilitative aids. Researchers are taking keen interest to accurately classify the stimulated electroencephalography (EEG) signals to interpret motor imagery tasks. In this paper we aim to classify the finger-, elbow- and shoulder-classification along with left- and right-hand classification to move a simulated robot arm in 3D space towards a target of known location. The contribution of the paper lies in the design of an energy optimal trajectory planner, based on differential evolution, which would decide the optimal path for the robot arm to move towards the target based on the classifier output. Each different set of movements consists of a trajectory planner which is activated by the classifier output. The energy distribution of wavelet coefficients of the incoming EEG signals is used as features to be used as inputs in a naïve Bayesian classifier to discriminate among the different mental tasks. The average training classification accuracy obtained is 76.88% and the success rate of the simulated robot arm reaching the target is 85%. 
A differential evolution based energy trajectory planner for artificial limb control using motor imagery EEG signal