Functional neuroimaging consists in the use of imaging technologies allowing to record the functional brain activity in real time. Among all techniques, data produced by functional magnetic resonance is encoded as sequences of 3D images of thousands of voxels. The main investigation performed on this data, termed brain mapping, aims at producing functional maps of the brain. Brain mapping aims at the detection of the portion of voxels concerned with specific perceptual or cognitive brain activities. This challenge can be shaped as a problem of feature selection. Excessive features-to-instances ratio characterizing this data is a major issue for the computation of statistically robust maps. We propose a solution based on a Random Subspace Method that extends the reference approach (Search Light) adopted by the neuroscientific community. A comparison of the two methods is supported by the results of an empirical evaluation.