The effect of decreasing the number of training and validations sets on offline accuracy. The average accuracy and standard deviation of 100 trainings per each of the 17 subjects is shown for each classifier. The amount of available data sets was reduced from 100% to 6%, and the data sets were randomized before each training. The 100% represents 48 training and 24 validation sets, each a feature vector extracted from a 200 ms time window with 50 ms time increment. The testing sets were kept constant (49 per movement). A statistical significant reduction of accuracy was found between each step for LDA and MLP, but only for the last two steps for RFN. This suggests that RFN allows considerable reductions of training data while conserving similar classification accuracy. For clarity in the graph, only the non-statistical significant differences are shown by “ # ”.