fMRI-based Decoding of Visual Information from Human Brain Activity: A Brief Review

Abstract One of the most significant challenges in neuroscience community is to understand how human brain works. Recent progress neuroimaging techniques have validated that it possible decode a person’s thoughts, memories, and emotions via functional magnetic resonance imaging (i.e., fMRI) since can measure neural activation brains with satisfied spatiotemporal resolutions. However, unprecedented scale complexity fMRI data presented critical computational bottlenecks requiring new scientific analytic tools. Given increasingly important role machine learning neuroscience, great many algorithms are analyze activities from data. In this paper, we mainly provide comprehensive up-to-date review methods for analyzing following three aspects, i.e., image alignment, activity pattern analysis, visual stimuli reconstruction. addition, online resources open research problems on analysis also provided convenience future research.