Ayse Ece Ercan1, Esin Karahan2, Onur Ozyurt2, Cengizhan Ozturk2
1Biomedical Engineering, TU Delft, Delft, Netherlands; 2Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
High dimensional feature space of fMRI volumes has been a drawback for classification studies since large feature dimension is known to increase the classification error and the computation time. In this study, we combined PCA with two anatomical feature selection methods: grey matter (GM) and region of interest (ROI) masking, and investigated the effects of different feature reduction methods on the classification accuracy of a linear SVM classifier. To apply PCA after anatomical masking is concluded to be a reliable method for preserving the classification accuracy of the anatomical feature selection methods and reducing the computation time.