# Efficient Multi Subject Visual Reconstruction from fMRI Using Aligned Representations

arXiv: https://arxiv.org/abs/2505.01670 · HTML: https://mlsanity.com/paper/2505.01670/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 50 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 21 of 21)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://mlsanity.com/paper/2211.08332/) (2022)
- [Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors](https://mlsanity.com/paper/2305.18274/) (2023)
- [MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data](https://mlsanity.com/paper/2403.11207/) (2024)
- [Mind Reader: Reconstructing complex images from brain activities](https://mlsanity.com/paper/2210.01769/) (2022)
- [Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion](https://mlsanity.com/paper/2403.07721/) (2024)
- [From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI](https://mlsanity.com/paper/1907.02431/) (2019)
- [MindBridge: A Cross-Subject Brain Decoding Framework](https://mlsanity.com/paper/2404.07850/) (2024)
- [MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion](https://mlsanity.com/paper/2308.04249/) (2023)
- [Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN](https://mlsanity.com/paper/2001.11761/) (2020)
- [DREAM: Visual Decoding from Reversing Human Visual System](https://mlsanity.com/paper/2310.02265/) (2023)
- [Aligning individual brains with Fused Unbalanced Gromov-Wasserstein](https://mlsanity.com/paper/2206.09398/) (2022)
- [Through their eyes: multi-subject Brain Decoding with simple alignment techniques](https://mlsanity.com/paper/2309.00627/) (2023)
- [fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding](https://mlsanity.com/paper/2311.00342/) (2023)
- [Aligning brain functions boosts the decoding of visual semantics in novel subjects](https://mlsanity.com/paper/2312.06467/) (2023)
- [See Through Their Minds: Learning Transferable Neural Representation from Cross-Subject fMRI](https://mlsanity.com/paper/2403.06361/) (2024)

## Built on by (most cited first, 2 of 2)

- [EmoMind: Decoding Affective Captions from Human Brain fMRI](https://mlsanity.com/paper/2605.16739/) (2026)
- [MindAlign: Decoding Inner Speech from fMRI Signals via Multimodal Embedding Alignment under Limited Data](https://mlsanity.com/paper/2606.20696/) (2026)

## Abstract (arXiv)

This work introduces a novel approach to fMRI-based visual image reconstruction using a subject-agnostic common representation space. We show that the brain signals of the subjects can be aligned in this common space during training to form a semantically aligned common brain. This is leveraged to demonstrate that aligning subject-specific lightweight modules to a reference subject is significantly more efficient than traditional end-to-end training methods. Our approach excels in low-data scenarios. We evaluate our methods on different datasets, demonstrating that the common space is subject and dataset-agnostic.