Voxel Selection of fMRI Data using Multi- Voxel Pattern Analysis to Predict Neural Response
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چکیده
fMRI system shows that all the information of the brain that is represented in the subject of the brain at a particular point in time. The MVPA approach has lead to several impressive facts of brain reading. fMRI data uses Multivoxel pattern analysis (MVPA) approach to relate the neural activities to cognition. A challenging factor is to build a generalizable classification model because the number of voxels (features) always exceeds the total number of stimulus/data observations, creating model overfitting. Thus selecting informative voxels should be done before constructing a classification model. In this paper, we propound an effective feature (voxel) selection strategy using partial least square regression (PLS) to form an index for the informative voxels to prioritize the voxel selection based on the degree of association to the stimulus conditions. IndexTerms— Multi-voxel pattern analysis (MVPA), functional magnetic resonance imaging (fMRI), partial least square (PLS).
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