通过频谱对齐提升生物信号跨被试泛化能力
BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series
- 提出频带对齐模块，动态调节频段幅度与相位以对齐信号结构
- 在6个数据集上实现平均F1提升6%，优于12个基线方法
- 适合需要跨被试泛化的医疗时序分析场景
生物医学时序信号的跨被试泛化指在部分被试数据上训练，测试于未见被试。核心挑战在于抑制表示中的个体差异。现有方法多通过模型设计或对抗学习隐式抑制，极少显式建模。本文引入频谱漂移新视角：相同标签的信号常具有一致振荡结构，但在特定频段存在受个体影响的幅度或相位偏移。基于此，提出BioFormer，其核心为频带对齐模块（FBAM），从频谱分布生成带内调制因子，自适应调整幅度与相位以对齐频谱结构。进一步结合样本条件层归一化，从信号内在统计量推导归一化参数而非依赖被试身份，稳定跨被试表示。在6个数据集上的实验表明，BioFormer超越12个基线，绝对F1得分提升6%。
原文摘要 · Abstract (English)
Cross-subject generalization in biomedical time-series refers to training on data from some subjects and testing on unseen subjects.The key challenge is to suppress subject specific variability in BTS representations.Most existing methods implicitly suppress the variability through model building or subject adversarial learning, but rarely model it explicitly.We introduce spectral drift as a new perspective to characterize subject specific variability.Specifically, BTS signals under the same label often share consistent oscillatory structure, yet exhibit subject-dependent magnitude or phase shifts in specific frequency components, which we interpret as subject-specific variability. Building on this insight, we propose BioFormer.At its core is a Frequency-Band Alignment Module(FBAM) that generates band-wise modulation factors from the spectral distribution and adaptively adjusts amplitude and phase to align spectral structure, thereby mitigating variability.We further pair FBAM with Sample Conditional Layer Normalization, which infers normalization parameters from intrinsic signal statistics rather than subject identity, stabilizing cross-subject representations.Extensive experiments on six datasets demonstrate that BioFormer outperforms 12 baselines, yielding absolute F1-score improvements of 6%.
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