Comparison of classification and dimensionality reduction methods used in fMRI decoding

In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve the classification performance; therefore, seven methods in region of interest (RDI) have been compared to answer the following question: which dimensionality reduction procedure performs best? In both tasks, in addition to measuring prediction accuracy, we estimated standard deviation of accuracies to realize more reliable methods. According to all results, we suggest using support vector machines with linear kernel (C-SVM and v-SVM), or random forest classifier on low dimensional subsets, which is prepared by Active or maxDis feature selection method to classify brain activity patterns more efficiently.