Abstract
Feature engineering techniques such as feature selection and extraction dominate the process of cognitive state learning. The extraction of relevant features from high-dimensional multi-way functional MRI (fMRI) data is essential for the classification of a cognitive task. The dimensionality of fMRI influences the analysis of brain data. fMRI data is arranged as a number of voxels, region of interests (ROI) and snapshots. The extraction of a specific pattern of interest within the noisy components is a challenging task. In this paper, a tensor gradient-based feature extraction technique decomposes the multi-way fMRI data into a number of components. Voxel time series data from different ROIs has been used to find the region of discrimination. Clustering-based maximum margin feature selection method has been proposed to select the minimum number of voxels as attributes. The proposed techniques provide a better learning accuracy for the StarPlus fMRI data.
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