BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities
Zhibo Tian, Ruijie Quan, Fan Ma, Kun Zhan, Yi Yang
Abstract
Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused on training separate models for each individual to account for individual variability in brain activity, overlooking valuable cross-subject commonalities. Recent advancements have explored multisubject methods, but these approaches face significant challenges, particularly in data privacy and effectively managing individual variability. To overcome these challenges, we introduce BrainGuard, a privacy-preserving collaborative training framework designed to enhance image reconstruction from multisubject fMRI data while safeguarding individual privacy. BrainGuard employs a collaborative global-local architecture where personalized models are trained on each subject's data and operate in conjunction with a shared commonality model that captures and leverages cross-subject patterns. This architecture eliminates the need to aggregate fMRI data across subjects, thereby ensuring privacy preservation. To tackle the complexity of fMRI data, BrainGuard integrates a hybrid synchronization strategy, enabling individual models to dynamically incorporate parameters from the global model. By establishing a secure and collaborative training environment, BrainGuard not only protects sensitive brain activity data but also improves the accuracy of image reconstructions. Extensive experiments demonstrate that BrainGuard sets a new benchmark in both high-level and low-level metrics, advancing the state-of-the-art in brain decoding through its innovative design.
BibTeX
@article{Tian_Quan_Ma_Zhan_Yang_2025, title={BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33579}, DOI={10.1609/aaai.v39i13.33579}, abstractNote={Reconstructing perceived images from human brain activity forms a crucial link between human and machine learning through Brain-Computer Interfaces. Early methods primarily focused on training separate models for each individual to account for individual variability in brain activity, overlooking valuable cross-subject commonalities. Recent advancements have explored multisubject methods, but these approaches face significant challenges, particularly in data privacy and effectively managing individual variability. To overcome these challenges, we introduce BrainGuard, a privacy-preserving collaborative training framework designed to enhance image reconstruction from multisubject fMRI data while safeguarding individual privacy. BrainGuard employs a collaborative global-local architecture where personalized models are trained on each subject’s data and operate in conjunction with a shared commonality model that captures and leverages cross-subject patterns. This architecture eliminates the need to aggregate fMRI data across subjects, thereby ensuring privacy preservation. To tackle the complexity of fMRI data, BrainGuard integrates a hybrid synchronization strategy, enabling individual models to dynamically incorporate parameters from the global model. By establishing a secure and collaborative training environment, BrainGuard not only protects sensitive brain activity data but also improves the accuracy of image reconstructions. Extensive experiments demonstrate that BrainGuard sets a new benchmark in both high-level and low-level metrics, advancing the state-of-the-art in brain decoding through its innovative design.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Tian, Zhibo and Quan, Ruijie and Ma, Fan and Zhan, Kun and Yang, Yi}, year={2025}, month={Apr.}, pages={14414-14422} }