LEA: Learning Latent Embedding Alignment Model for fMRI Decoding and Encoding
Primary Area: applications to neuroscience & cognitive science
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Keywords: Neural Encoding, Neural Decoding, Latent Embedding Alignment
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Abstract: The connection between brain activity and visual stimuli is crucial to understand the human brain. While deep generative models have exhibited advances in recovering brain recordings by generating images conditioned on fMRI signals, it is still challenge to generate consistent semantics. Moreover, the prediction of fMRI signal from visual stimuli remains a hard problem. In this paper, we introduce a unified framework that addresses both fMRI decoding and encoding. With training two latent spaces capable of representing and reconstructing fMRI signals and visual images, respectively, we align the fMRI signals and visual images within the latent space, thereby enabling a bidirectional transformation between the two domains. Our Latent Embedding Alignment (LEA) model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework under user-specified direction. The performance of LEA surpasses that of existing methods on multiple benchmark fMRI decoding and encoding datasets. LEA offers a comprehensive solution for modeling the relationship between fMRI signal and visual stimuli.
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Submission Number: 1748
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