Using Deep Learning for Visual Decoding and Reconstruction from Brain Activity: A Review

TL;DR

This review discusses how deep learning techniques are used for reconstructing images from brain activity data, highlighting challenges and the importance of architecture choices and feature representation.

Contribution

It provides a comprehensive overview of deep learning applications in visual decoding from fMRI data and evaluates the impact of different architectures and features.

Findings

Deep learning improves image reconstruction quality from brain data.

Architectural choices significantly affect decoding performance.

Feature representation is crucial for effective visual decoding.

Abstract

This literature review will discuss the use of deep learning methods for image reconstruction using fMRI data. More specifically, the quality of image reconstruction will be determined by the choice in decoding and reconstruction architectures. I will show that these structures can struggle with adaptability to various input stimuli due to complicated objects in images. Also, the significance of feature representation will be evaluated. This paper will conclude the use of deep learning within visual decoding and reconstruction is highly optimal when using variations of deep neural networks and will provide details of potential future work.