  The human brain is a highly efficient processing unit, and understanding how
it works can inspire new algorithms and architectures in machine learning. In
this work, we introduce a novel framework named Brain Activation Network
(BRACTIVE), a transformer-based approach to studying the human visual brain.
The main objective of BRACTIVE is to align the visual features of subjects with
corresponding brain representations via fMRI signals. It allows us to identify
the brain's Regions of Interest (ROI) of the subjects. Unlike previous brain
research methods, which can only identify ROIs for one subject at a time and
are limited by the number of subjects, BRACTIVE automatically extends this
identification to multiple subjects and ROIs. Our experiments demonstrate that
BRACTIVE effectively identifies person-specific regions of interest, such as
face and body-selective areas, aligning with neuroscience findings and
indicating potential applicability to various object categories. More
importantly, we found that leveraging human visual brain activity to guide deep
neural networks enhances performance across various benchmarks. It encourages
the potential of BRACTIVE in both neuroscience and machine intelligence
studies.
