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

Yuqin Dai, Zhouheng Yao, Chunfeng Song, Qihao Zheng, Weijian Mai, Kunyu Peng, Shuai Lu, Wanli Ouyang, Jian Yang, Jiamin Wu
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:12214-12228, 2025.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-dai25m, title = {{M}ind{A}ligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited f{MRI} Data}, author = {Dai, Yuqin and Yao, Zhouheng and Song, Chunfeng and Zheng, Qihao and Mai, Weijian and Peng, Kunyu and Lu, Shuai and Ouyang, Wanli and Yang, Jian and Wu, Jiamin}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {12214--12228}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/dai25m/dai25m.pdf}, url = {https://proceedings.mlr.press/v267/dai25m.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data %A Yuqin Dai %A Zhouheng Yao %A Chunfeng Song %A Qihao Zheng %A Weijian Mai %A Kunyu Peng %A Shuai Lu %A Wanli Ouyang %A Jian Yang %A Jiamin Wu %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-dai25m %I PMLR %P 12214--12228 %U https://proceedings.mlr.press/v267/dai25m.html %V 267 %X 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.
APA
Dai, Y., Yao, Z., Song, C., Zheng, Q., Mai, W., Peng, K., Lu, S., Ouyang, W., Yang, J. & Wu, J.. (2025). MindAligner: Explicit Brain Functional Alignment for Cross-Subject Visual Decoding from Limited fMRI Data. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:12214-12228 Available from https://proceedings.mlr.press/v267/dai25m.html.

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