Decoding natural image stimuli from fMRI data with a surface-based convolutional network

• URL: http://arxiv.org/abs/2212.02409v1
• Date: Mon, 5 Dec 2022 16:47:19 GMT
• Title: Decoding natural image stimuli from fMRI data with a surface-based convolutional network
• Authors: Zijin Gu, Keith Jamison, Amy Kuceyeski and Mert Sabuncu
• Abstract summary: We propose a novel approach to decode visual stimuli with high semantic fidelity and rich fine-grained detail. Our proposed method achieves state-of-the-art semantic fidelity, while yielding good fine-grained similarity with the ground-truth stimulus.
• Score: 1.8352113484137629
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Due to the low signal-to-noise ratio and limited resolution of functional MRI data, and the high complexity of natural images, reconstructing a visual stimulus from human brain fMRI measurements is a challenging task. In this work, we propose a novel approach for this task, which we call Cortex2Image, to decode visual stimuli with high semantic fidelity and rich fine-grained detail. In particular, we train a surface-based convolutional network model that maps from brain response to semantic image features first (Cortex2Semantic). We then combine this model with a high-quality image generator (Instance-Conditioned GAN) to train another mapping from brain response to fine-grained image features using a variational approach (Cortex2Detail). Image reconstructions obtained by our proposed method achieve state-of-the-art semantic fidelity, while yielding good fine-grained similarity with the ground-truth stimulus. Our code is available at: https://github.com/zijin-gu/meshconv-decoding.git.

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