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

Shuo Huang, Wei Shao, Mei-Ling Wang, Dao-Qiang Zhang
2021 International Journal of Automation and Computing  
AbstractOne of the most significant challenges in the neuroscience community is to understand how the human brain works. Recent progress in neuroimaging techniques have validated that it is possible to decode a person's thoughts, memories, and emotions via functional magnetic resonance imaging (i.e., fMRI) since it can measure the neural activation of human brains with satisfied spatiotemporal resolutions. However, the unprecedented scale and complexity of the fMRI data have presented critical
more » ... omputational bottlenecks requiring new scientific analytic tools. Given the increasingly important role of machine learning in neuroscience, a great many machine learning algorithms are presented to analyze brain activities from the fMRI data. In this paper, we mainly provide a comprehensive and up-to-date review of machine learning methods for analyzing neural activities with the following three aspects, i.e., brain image functional alignment, brain activity pattern analysis, and visual stimuli reconstruction. In addition, online resources and open research problems on brain pattern analysis are also provided for the convenience of future research.
doi:10.1007/s11633-020-1263-y fatcat:kwls2cvw4zgd5dti5d54uy6pgi