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arXiv · 2501.14309

BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities

Zhibo Tian·Ruijie Quan·Fan Ma·Kun Zhan·Yi Yang
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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 individual models are trained on each subject's local data and operate in conjunction with a shared global 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 data but also improves the image reconstructions accuracy. 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.

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Zhibo Tian, Ruijie Quan, Fan Ma, Kun Zhan, Yi Yang. 2025-01-24. BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities. https://arxiv.org/abs/2501.14309

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