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.