Feature selection based on synchronization analysis for multiple fMRI data

Abstract

Functional magnetic resonance imaging (fMRI) can be used to predict the states of the human brain. However, solving the learning problem in multi-subjects is difficult, because of the inter-subject variability. In this paper, we use the synchronization of fMRI voxels when the brain responds to a stimulus in order to construct features for achieving better data representation and more efficient classification. With a simple definition of synchronization, the proposed method is insensitive to the reasonable choices over a broad range of thresholds. We also demonstrate a new unbiased method to compare multiple subjects by applying the singular value decomposition (SVD) to the discrimination matrix, which enumerates the different patterns. The method for analyzing the fMRI data works well for identifying the meaningful functional differences between subjects.