Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI

• URL: http://arxiv.org/abs/2312.00236v2
• Date: Wed, 29 May 2024 07:34:59 GMT
• Title: Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI
• Authors: Xuan-Bac Nguyen, Xin Li, Pawan Sinha, Samee U. Khan, Khoa Luu,
• Abstract summary: We introduce a novel framework named Brainformer to analyze fMRI patterns in the human perception system. This work introduces a prospective approach to transfer knowledge from human perception to neural networks.
• Score: 12.203617776046169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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