Statistical Learning for Latent Embedding Alignment with Application to Brain Encoding and Decoding
Brain encoding and decoding aims to understand the relationship between external stimuli and brain activities, and is a fundamental problem in neuroscience. In this article, we study latent embedding alignment for brain encoding and decoding, with a focus on improving sample efficiency under limited fMRI-stimulus paired data and substantial subject heterogeneity. We propose a lightweight alignment framework equipped with two statistical learning components: inverse semi-supervised learning that leverages abundant unpaired stimulus embeddings through inverse mapping and residual debiasing, and meta transfer learning that borrows strength from pretrained models across subjects via sparse ag...
| Authors | Shuoxun Xu, Zhanhao Yan, Lexin Li |
|---|---|
| Primary category | stat.ME |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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