  Multivariate Pattern (MVP) classification holds enormous potential for
decoding visual stimuli in the human brain by employing task-based fMRI data
sets. There is a wide range of challenges in the MVP techniques, i.e.
decreasing noise and sparsity, defining effective regions of interest (ROIs),
visualizing results, and the cost of brain studies. In overcoming these
challenges, this paper proposes a novel model of neural representation, which
can automatically detect the active regions for each visual stimulus and then
utilize these anatomical regions for visualizing and analyzing the functional
activities. Therefore, this model provides an opportunity for neuroscientists
to ask this question: what is the effect of a stimulus on each of the detected
regions instead of just study the fluctuation of voxels in the manually
selected ROIs. Moreover, our method introduces analyzing snapshots of brain
image for decreasing sparsity rather than using the whole of fMRI time series.
Further, a new Gaussian smoothing method is proposed for removing noise of
voxels in the level of ROIs. The proposed method enables us to combine
different fMRI data sets for reducing the cost of brain studies. Experimental
studies on 4 visual categories (words, consonants, objects and nonsense photos)
confirm that the proposed method achieves superior performance to
state-of-the-art methods.
