Fast voxel selection of fMRI data based on Smoothed 10 norm

Abstract

Feature selection (FS) plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI based decoding due to the “few samples and large features” of fMRI data. The multivariate FS methods are generally time-consuming although they displayed better performance than the univariate FS methods. In this study, we applied a fast sparse representation method based on Smoothed 10 (SLO) algorithm to select relevant features in fMRI data. The performance of Gaussian Naive Bayes (GNB) classifier using voxels selected by SLO and the univariate t-test methods were also compared. Results of both simulated and real fMRI experiments demonstrated that the SLO method largely improved the classification accuracy of GNB compared to the t-test method for all the noise levels.