# MindBridge: A Cross-Subject Brain Decoding Framework

arXiv: https://arxiv.org/abs/2404.07850 · HTML: https://mlsanity.com/paper/2404.07850/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 52 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 30 of 34)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [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)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [UniPC: A Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models](https://mlsanity.com/paper/2302.04867/) (2023)
- [DeepCache: Accelerating Diffusion Models for Free](https://mlsanity.com/paper/2312.00858/) (2023)
- [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://mlsanity.com/paper/2211.08332/) (2022)
- [Structural Pruning for Diffusion Models](https://mlsanity.com/paper/2305.10924/) (2023)
- [Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors](https://mlsanity.com/paper/2305.18274/) (2023)
- [Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding](https://mlsanity.com/paper/2211.06956/) (2022)
- [Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models](https://mlsanity.com/paper/2304.12526/) (2023)
- [Diffusion Probabilistic Model Made Slim](https://mlsanity.com/paper/2211.17106/) (2022)
- [Generic decoding of seen and imagined objects using hierarchical visual features](https://mlsanity.com/paper/1510.06479/) (2015)
- [Diffusion Model as Representation Learner](https://mlsanity.com/paper/2308.10916/) (2023)
- [From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI](https://mlsanity.com/paper/1907.02431/) (2019)
- [Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANs](https://mlsanity.com/paper/2202.12692/) (2022)
- [Decoding natural image stimuli from fMRI data with a surface-based convolutional network](https://mlsanity.com/paper/2212.02409/) (2022)
- [MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion](https://mlsanity.com/paper/2308.04249/) (2023)
- [DREAM: Visual Decoding from Reversing Human Visual System](https://mlsanity.com/paper/2310.02265/) (2023)

## Built on by (most cited first, 30 of 66)

- [Bridging the Vision-Brain Gap with an Uncertainty-Aware Blur Prior](https://mlsanity.com/paper/2503.04207/) (2025)
- [The Wisdom of a Crowd of Brains: A Universal Brain Encoder](https://mlsanity.com/paper/2406.12179/) (2024)
- [MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI](https://mlsanity.com/paper/2503.02351/) (2025)
- [Towards a general-purpose foundation model for fMRI analysis](https://mlsanity.com/paper/2506.11167/) (2025)
- [MindLLM: A Subject-Agnostic and Versatile Model for fMRI-to-Text Decoding](https://mlsanity.com/paper/2502.15786/) (2025)
- [BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities](https://mlsanity.com/paper/2501.14309/) (2025)
- [Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.13378/) (2024)
- [A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli](https://mlsanity.com/paper/2503.15978/) (2025)
- [MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding](https://mlsanity.com/paper/2505.15946/) (2025)
- [ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding](https://mlsanity.com/paper/2510.27128/) (2025)
- [A Survey of fMRI to Image Reconstruction](https://mlsanity.com/paper/2502.16861/) (2025)
- [Reanimating Images using Neural Representations of Dynamic Stimuli](https://mlsanity.com/paper/2406.02659/) (2024)
- [Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI](https://mlsanity.com/paper/2505.14556/) (2025)
- [Scaling Vision Transformers for Functional MRI with Flat Maps](https://mlsanity.com/paper/2510.13768/) (2025)
- [Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer](https://mlsanity.com/paper/2510.25976/) (2025)
- [MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding](https://mlsanity.com/paper/2405.17720/) (2024)
- [COBRA: A Continual Learning Approach to Vision-Brain Understanding](https://mlsanity.com/paper/2411.17475/) (2024)
- [Exploring The Visual Feature Space for Multimodal Neural Decoding](https://mlsanity.com/paper/2505.15755/) (2025)
- [Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval](https://mlsanity.com/paper/2604.17782/) (2026)
- [Learning Brain Representation with Hierarchical Visual Embeddings](https://mlsanity.com/paper/2602.07495/) (2026)
- [Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing](https://mlsanity.com/paper/2503.10663/) (2025)
- [Efficient Multi Subject Visual Reconstruction from fMRI Using Aligned Representations](https://mlsanity.com/paper/2505.01670/) (2025)
- [Multigranular Evaluation for Brain Visual Decoding](https://mlsanity.com/paper/2507.07993/) (2025)
- [From Flat to Round: Redefining Brain Decoding with Surface-Based fMRI and Cortex Structure](https://mlsanity.com/paper/2507.16389/) (2025)
- [Language Reconstruction with Brain Predictive Coding from fMRI Data](https://mlsanity.com/paper/2405.11597/) (2024)
- [Cross-Subject Mind Decoding from Inaccurate Representations](https://mlsanity.com/paper/2507.19071/) (2025)
- [MindShot: Multi-Shot Video Reconstruction from fMRI with LLM Decoding](https://mlsanity.com/paper/2508.02480/) (2025)
- [Image-to-Brain Signal Generation for Visual Prosthesis with CLIP Guided Multimodal Diffusion Models](https://mlsanity.com/paper/2509.00787/) (2025)
- [Decoding Visual Neural Representations by Multimodal with Dynamic Balancing](https://mlsanity.com/paper/2509.03433/) (2025)
- [Towards Interpretable Visual Decoding with Attention to Brain Representations](https://mlsanity.com/paper/2509.23566/) (2025)

## Abstract (arXiv)

Brain decoding, a pivotal field in neuroscience, aims to reconstruct stimuli from acquired brain signals, primarily utilizing functional magnetic resonance imaging (fMRI). Currently, brain decoding is confined to a per-subject-per-model paradigm, limiting its applicability to the same individual for whom the decoding model is trained. This constraint stems from three key challenges: 1) the inherent variability in input dimensions across subjects due to differences in brain size; 2) the unique intrinsic neural patterns, influencing how different individuals perceive and process sensory information; 3) limited data availability for new subjects in real-world scenarios hampers the performance of decoding models. In this paper, we present a novel approach, MindBridge, that achieves cross-subject brain decoding by employing only one model. Our proposed framework establishes a generic paradigm capable of addressing these challenges by introducing biological-inspired aggregation function and novel cyclic fMRI reconstruction mechanism for subject-invariant representation learning. Notably, by cycle reconstruction of fMRI, MindBridge can enable novel fMRI synthesis, which also can serve as pseudo data augmentation. Within the framework, we also devise a novel reset-tuning method for adapting a pretrained model to a new subject. Experimental results demonstrate MindBridge's ability to reconstruct images for multiple subjects, which is competitive with dedicated subject-specific models. Furthermore, with limited data for a new subject, we achieve a high level of decoding accuracy, surpassing that of subject-specific models. This advancement in cross-subject brain decoding suggests promising directions for wider applications in neuroscience and indicates potential for more efficient utilization of limited fMRI data in real-world scenarios. Project page: https://littlepure2333.github.io/MindBridge