Figure 4.

Classification procedure. Representation of one iteration of the feature selection and machine learning procedure. For each iteration, one subject was defined as the test set, whereas the remaining N-1 subjects made up the training set. Each training set was then fed into the TFNBS to calculate the significant connections between groups. Subsequently, the significant connections were introduced into a recursive feature elimination (RFE) algorithm to select the optimal connections. The support vector machine (SVM) algorithm was then tuned with the selected features. The resulting classifier model was then used to classify the corresponding test subject.