The classification accuracy in a myoelectric control system depends on choosing the optimal features that represent
surface electromyographic (sEMG) signal, and selecting robust and fast classification algorithm. In this work, eight
hand motions were classified using different extracted features from sEMG signals. The results of the experiment show
that the classification rate of 97.41% was achieved using wavelet coefficients as feature vector and general regression neural
network (GRNN) classifier. In addition, we found that the combination of sample entropy (SampEnt), root mean
square (RMS), myopulse percentage rate (MYOP), and difference absolute standard deviation value (DASDV) achieved
the highest classification rate of 95.68% using multilayer perceptron neural network (MLPNN) classifier.