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In this paper, we present an effective computational approach for learning patterns of brain activity from the fMRI data. The procedure involved correcting motion artifacts, spatial smoothing, removing low frequency drifts and applying multivariate linear and non-linear kernel methods. Two novel techniques are applied: one utilizes the Cosine Transform to remove low-frequency drifts over time and the other involves using prior knowledge about the spatial contribution of different brain regionsdoi:10.1109/ijcnn.2008.4633870 dblp:conf/ijcnn/NiCSA08 fatcat:5t5bykhx7jhhzk4t4z4cvh2sqm