  Human perception plays a vital role in forming beliefs and understanding
reality. A deeper understanding of brain functionality will lead to the
development of novel deep neural networks. In this work, we introduce a novel
framework named Brainformer, a straightforward yet effective Transformer-based
framework, to analyze Functional Magnetic Resonance Imaging (fMRI) patterns in
the human perception system from a machine-learning perspective. Specifically,
we present the Multi-scale fMRI Transformer to explore brain activity patterns
through fMRI signals. This architecture includes a simple yet efficient module
for high-dimensional fMRI signal encoding and incorporates a novel embedding
technique called 3D Voxels Embedding. Secondly, drawing inspiration from the
functionality of the brain's Region of Interest, we introduce a novel loss
function called Brain fMRI Guidance Loss. This loss function mimics brain
activity patterns from these regions in the deep neural network using fMRI
data. This work introduces a prospective approach to transfer knowledge from
human perception to neural networks. Our experiments demonstrate that
leveraging fMRI information allows the machine vision model to achieve results
comparable to State-of-the-Art methods in various image recognition tasks.
