Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity

Figure 1

(a) Classification accuracies for support vector machine (SVM), multilayer perceptron (MP), radial basis function (RBF), and linear network (LN) averaged over all subjects; (b) position of electrodes according to extended 10-10 international system on human head; and (c) general model of ANN, where each input neuron receives data from one of electrodes; and are neurons of hidden layers, and is output neuron. The horizontal bars with asterisk show that RBF classification accuracy significantly exceeded the accuracy rates of both SVM and MLP according to the statistical analysis using paired t-test.