Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique used to capture images of brain activity. These images have high spatial resolution and hence are very high dimensional. Each scan consists of more than one hundred thousand voxels. All of the scanned voxels are not activated for every stimulus. Therefore, finding the informative voxels with respect to stimulus becomes a prerequisite for any machine learning solution using fMRI data. The specific problem attempted to be solved in this paper is that of decoding cognitive states from multiple-subject fMRI data. Decoding multiplesubject data is challenging owing to the difference in the shape and size of the brain of different subjects. A Genetic algorithm based technique is proposed here for selection of voxels that capture commonality across subjects. Some popular feature selection techniques are compared against Genetic algorithms. It is observed that feature selection using Genetic algorithms perform consistently and predictably better than other techniques.
Mind reading or thought prediction is a promising application of functional neuroimaging studies. The emergence of functional magnetic resonance imaging (fMRI) has, in the last two decades given a boost to these studies. In order to improve the accuracy, predictability and repeatability of thought prediction, it is important to have a representation that can capture the nuances of fMRI activations with respect to a particular cognitive state. In this paper, the process of creating a geometrical representation of the activations using non-linear manifolds is described. Manifold learning brings out the geometry of the activated voxels in the fMRI image. It is shown that this kind of representation is able to give high accuracy in classification studies as compared to using activation profiles.
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