Özmen, GüzinÖzsen, Seral2024-10-032024-10-032018978-605-68537-3-9https://hdl.handle.net/20.500.13091/6315The functional MR images consist of very high dimensional data containing thousands of voxels, even for a single subject. Data reduction methods are inevitable for the classification of these three-dimensional images. In the first step of the data reduction, the first level statistical analysis was applied to fMRI data and brain maps of each subject were obtained for the feature extraction. The second step is the feature selection. According to the feature selection method used in the classification studies of fMRI and which is called as the active method, the intensity values of all brain voxels are ranked from high to low and some of these features are presented to the classifier. However, the location information of the voxels is lost with this method. In this study, a new feature extraction method was presented for use in the classification of fMRI. According to this method, active voxels can be used as features by considering brain maps obtained in three dimensions as slice based. Since the functional MR images have big data sets, the selected features were once again reduced by Principal Component Analysis and the voxel intensity values were presented to the classifiers. As a result; 83.9% classification accuracy was obtained by using kNN classifier with purposed slicebased feature extraction method and it was seen that the slice-based feature extraction method increased the classification accuracy against the active method.eninfo:eu-repo/semantics/openAccessClassificationFeature ExtractionfMRISPMConference Object