# MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data

arXiv: https://arxiv.org/abs/2502.05034 · HTML: https://mlsanity.com/paper/2502.05034/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2025.

## Built on by (most cited first, 18 of 18)

- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment](https://mlsanity.com/paper/2511.06836/) (2025)
- [SynBrain: Enhancing Visual-to-fMRI Synthesis via Probabilistic Representation Learning](https://mlsanity.com/paper/2508.10298/) (2025)
- [Scaling Vision Transformers for Functional MRI with Flat Maps](https://mlsanity.com/paper/2510.13768/) (2025)
- [Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex](https://mlsanity.com/paper/2505.15813/) (2025)
- [Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval](https://mlsanity.com/paper/2604.17782/) (2026)
- [Improving Multimodal Brain Encoding Model with Dynamic Subject-awareness Routing](https://mlsanity.com/paper/2510.04670/) (2025)
- [SynMind: Reducing Semantic Hallucination in fMRI-Based Image Reconstruction](https://mlsanity.com/paper/2601.17857/) (2026)
- [Duala: Dual-Level Alignment of Subjects and Stimuli for Cross-Subject fMRI Decoding](https://mlsanity.com/paper/2603.07625/) (2026)
- [Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding](https://mlsanity.com/paper/2604.08537/) (2026)
- [Optimizing fMRI Data Acquisition for Decoding Natural Speech with Limited Participants](https://mlsanity.com/paper/2505.21304/) (2025)
- [GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework](https://mlsanity.com/paper/2512.00406/) (2025)
- [NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity](https://mlsanity.com/paper/2604.09817/) (2026)
- [StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation](https://mlsanity.com/paper/2605.02586/) (2026)
- [MindAdapter: Few-Shot Parameter-Efficient Residual Calibration of Cross-Subject Brain-to-Visual Decoding Models](https://mlsanity.com/paper/2605.24679/) (2026)
- [Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE](https://mlsanity.com/paper/2606.15989/) (2026)
- [MindAU: EEG-Conditioned Facial Action Unit Editing via Dual-Stream Manifold Alignment](https://mlsanity.com/paper/2607.00410/) (2026)
- [Lightweight Semantic EEG Foundation Model for Frozen Cross-Disorder Transfer](https://mlsanity.com/paper/2610.05503/) (2026)

## Abstract (arXiv)

Brain decoding aims to reconstruct visual perception of human subject from fMRI signals, which is crucial for understanding brain's perception mechanisms. Existing methods are confined to the single-subject paradigm due to substantial brain variability, which leads to weak generalization across individuals and incurs high training costs, exacerbated by limited availability of fMRI data. To address these challenges, we propose MindAligner, an explicit functional alignment framework for cross-subject brain decoding from limited fMRI data. The proposed MindAligner enjoys several merits. First, we learn a Brain Transfer Matrix (BTM) that projects the brain signals of an arbitrary new subject to one of the known subjects, enabling seamless use of pre-trained decoding models. Second, to facilitate reliable BTM learning, a Brain Functional Alignment module is proposed to perform soft cross-subject brain alignment under different visual stimuli with a multi-level brain alignment loss, uncovering fine-grained functional correspondences with high interpretability. Experiments indicate that MindAligner not only outperforms existing methods in visual decoding under data-limited conditions, but also provides valuable neuroscience insights in cross-subject functional analysis. The code will be made publicly available.