A Novel Approach of Dynamic Vision Reconstruction from fMRI Profiles Using Siamese Conditional Generative Adversarial Network

Abstract

: This paper aims to improve the quality of reconstructed visual stimuli and reduce the computational complexity of the visual stimuli reconstruction processes in the form of functional Magnetic Resonance Imaging (fMRI) profiles. The preceding work envisions the non-cognitive contents of brain activity vain to integrate visual data of diverse hierarchical levels. Existing approaches such as Deep Canonically Correlated Auto Encoder detect the significant challenges of reconstructing visual stimuli from brain activity: fMRI noise, large dimensionality of a limited number of data instances, and complex structure of visual stimuli. In this activity, we will also analyze the scope for utilizing the spatiotemporal data to resolve the neural correlates of visual stimulus representations and reconstruct the resembling visual stimuli. The purpose of this work is to manipulate those suffering from developmental disabilities.