I have a small dataset (117 training examples) and many features (4005). Each of the training examples is binary labeled (healthy / diseased). Each feature represents the connectivity between two different brain regions.
The goal is to assign subjects to one of the two groups based on their brain activity.
What methods are there for generating new artificial training examples based on the existing training examples?
An example I could think of would be SMOTE. However, this technique is usually only used to balance unbalanced datasets. This would not be necessary for my set, since it has about the same number of training examples for both label classes.