Multivariate pattern analysis techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Among various multivariate pattern analysis methods, sparse representation classifier (SRC) exhibit state-of-the-art classification performance for image classification. However, SRC has rarely been applied to fMRI-based decoding. This study aimed to investigate the feasibility of SRC in fMRI-based decoding and how to improve the performance of SRC. In this study, two SRC variants were proposed to improve SRC. We performed experimental tests on real fMRI data to compare the performance of SRC, the non-negative SRC (NSRC), two SRC variants, and the support vector machine (SVM). The results of the real fMRI experiments showed that the two SRC variants and NSRC exhibited much better classification performance than the SRC. Moreover, the performance of the second SRC variant is the best among the five classifiers.