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This paper proposes a rough set reduction scheme for Support Vector Machine (SVM). In the proposed scheme, SVM is used for the classification task based on the significance of each feature vector, while rough set is applied to improve feature selection and data reduction. Particle Swarm Optimization (PSO) is used to optimize the rough set feature reduction. The proposed approach is used to classify the brain cognitive state data sets from a cognitive Functional Magnetic Resonance Imaging (fMRI) experiment. Empirical results indicate that by using the proposed hybrid scheme it is feasible to achieve the desired classification very efficiently.