发表机构
Tsinghua University; School of Life Sciences, Tsinghua University（清华大学; 清华大学生命科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对EEG视觉解码中固定锚点语义易失配的问题，提出ProCA框架，通过渐进式对比对齐与结构一致插值，在多EEG设置中实现显著性能提升。
AI 中文摘要
脑电（EEG）视觉解码旨在从非侵入式神经时间序列信号中恢复视觉语义，其中噪声神经响应与稳定语义表征之间的鲁棒对齐是实现高性能解码的关键。尽管对比学习近年取得进展，但现有方法依赖固定视觉或文本锚点，其语义关系可能与跨试次、跨被试、跨学习阶段变化的EEG表征失配，导致鲁棒EEG解码仍具挑战性。本文实证表明，该不稳定性存在于标准EEG解码协议及更具挑战性的鲁棒性设置中，包括严格跨被试迁移与真实个性化持续适应。形式分析显示，当EEG特定关系演化时，固定语义监督会使优化产生偏差，而与结构无关的扰动可能扭曲EEG的语义重要组件。为解决这些问题，本文提出Progressive Contrastive Alignment（ProCA），一种统一且模型无关的自适应神经-语义对齐框架。ProCA从冻结的视觉-语言先验逐步细化类别级对比监督至EEG感知语义关系，并引入结构一致插值以按通道和时间重要性约束特征混合。在被试依赖、被试独立、严格跨被试迁移及持续适应设置中，ProCA分别实现Top-1/Top-5平均相对提升7.4%/3.9%、10.0%/4.6%、28.1%/17.8%、16.8%/11.6%。
英文摘要
Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-series signals, for which robust alignment between noisy neural responses and stable semantic representations is key to achieving high-performance decoding. Despite recent advances in contrastive learning, robust EEG decoding remains challenging because existing methods rely on fixed visual or textual anchors whose semantic relations may become misaligned with EEG representations that vary across trials, subjects, and learning stages. Our empirical evidence shows that this instability appears across both standard EEG decoding protocols and more challenging robustness settings, including strict cross-subject transfer and realistic personalized continual adaptation. We provide a formal analysis showing that fixed semantic supervision can bias optimization when EEG-specific relations evolve, and that structure-agnostic perturbations may distort semantically important EEG components. To address these issues, we propose Progressive Contrastive Alignment (ProCA), a unified and model-agnostic framework for adaptive neural-semantic alignment. ProCA progressively refines class-level contrastive supervision from frozen vision-language priors to EEG-aware semantic relations, and introduces structure-consistent interpolation to constrain feature mixing according to channel-wise and temporal importance. Across subject-dependent, subject-independent, strict cross-subject transfer, and continual adaptation settings, ProCA achieves average relative Top-1/Top-5 gains of 7.4%/3.9%, 10.0%/4.6%, 28.1%/17.8%, and 16.8%/11.6%, respectively.
