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Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity
Conference proceeding   Open access

Multiscale Voxel Based Decoding For Enhanced Natural Image Reconstruction From Brain Activity

Mali Halac, Murat Isik, Hasan Ayaz and Anup Das
International Joint Conference on Neural Networks (IJCNN)
01 Jan 2022
DOI: https://doi.org/10.1109/IJCNN55064.2022.9892430
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
http://arxiv.org/abs/2205.14177View
Submitted Open arXiv.org - Non-exclusive license to distribute

Abstract

Generative adversarial networks Image enhancement Image reconstruction Neural networks Brain Magnetic Resonance Imaging
Reconstructing perceived images from human brain activity monitored by functional magnetic resonance imaging (fMRI) is hard, especially for natural images. Existing methods often result in blurry and unintelligible reconstructions with low fidelity. In this study, we present a novel approach for enhanced image reconstruction, in which existing methods for object decoding and image reconstruction are merged together. This is achieved by conditioning the reconstructed image to its decoded image category using a class-conditional generative adversarial network and neural style transfer. The results indicate that our approach improves the semantic similarity of the reconstructed images and can be used as a general framework for enhanced image reconstruction.

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2 citations in Web of Science

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UN Sustainable Development Goals (SDGs)

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#3 Good Health and Well-Being

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Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Neurosciences
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