统一模型实现跨人脑解码，无需个体参数即可高效还原思维内容。
UniBrain: A Unified Model for Cross-Subject Brain Decoding
- 采用群体提取器与双向特征对齐，捕捉跨个体脑信号共性。
- 在基准测试中性能媲美顶尖个体模型，参数量大幅减少。
- 适合关注通用脑解码、减少个体建模负担的研究者。
脑解码旨在从fMRI信号中重建原始刺激，揭示心智内容。现有方法严重依赖个体特异性模型，因大脑处理机制复杂且个体间fMRI信号差异大，导致模型泛化能力差，难以捕捉跨个体共性。为此，我们提出UniBrain，一种无需个体参数的统一脑解码模型。该方法包括基于群体的提取器以处理可变长度的fMRI信号，互助嵌入模块以捕捉跨个体共性，以及双层特征对齐机制以提取个体无关特征。我们在脑解码基准上验证了UniBrain，性能可媲美当前最先进个体模型，且参数量极低。此外，我们提出一个泛化性基准，鼓励社区重视跨个体共性，推动更通用的脑解码研究。代码已开源：https://github.com/xiaoyao3302/UniBrain。
原文摘要 · Abstract (English)
Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific models due to the complex brain processing mechanisms and the variations in fMRI signals across individuals. Therefore, these methods greatly limit the generalization of models and fail to capture cross-subject commonalities. To address this, we present UniBrain, a unified brain decoding model that requires no subject-specific parameters. Our approach includes a group-based extractor to handle variable fMRI signal lengths, a mutual assistance embedder to capture cross-subject commonalities, and a bilevel feature alignment scheme for extracting subject-invariant features. We validate our UniBrain on the brain decoding benchmark, achieving comparable performance to current state-of-the-art subject-specific models with extremely fewer parameters. We also propose a generalization benchmark to encourage the community to emphasize cross-subject commonalities for more general brain decoding. Our code is available at https://github.com/xiaoyao3302/UniBrain.
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