对齐不完整：多模态脑电-眼动情绪识别的联合分布校准
Aligning the Incomplete: Joint Distribution Calibration for Multimodal EEG-Eye Emotion Recognition
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中文总结 AI 辅助
针对跨受试者多模态情绪识别中脑电与眼动数据不对称分布崩溃问题，提出GUARD方法，通过梯度加权锚定、感知损失生成和循环一致流实现联合分布校准，显著优于现有方法。
中文摘要 AI 辅助
跨受试者多模态情绪识别的成功取决于维持个体间联合数据分布的一致性。然而，实际部署中经常触发“不对称联合分布崩溃”：脑电信号遭受严重的跨受试者分布偏移，而眼动传感器易受数据包丢失和跟踪失败的影响。现有方法将领域适应和缺失模态填补视为不相关的任务，因此当两种退化同时发生时，它们无法解决复合误差，要么通过填补信号传播领域偏移，要么破坏联合决策边界。为应对这一统一挑战，我们提出GUARD（梯度引导的无监督不对称分布恢复）。首先，GUARD通过采用具有理论基础的梯度加权目标在源域建立可靠的锚定流形，迫使稳健的脑电模态预先纠缠任务判别性眼动特征。其次，为结构性恢复崩溃的联合分布，我们用下游感知损失约束生成模块，优先考虑情绪判别语义而非单纯信号保真度。最后，我们将目标域适应表述为不适定逆问题，通过驱动循环一致流，直接在目标域流形上实现恢复后联合分布的无监督校准。大量实验表明，GUARD显著优于最先进方法，即使在辅助模态完全失效时仍保持弹性判别性能。我们的代码和模型已公开，以确保完全可复现性。
英文摘要
The success of cross-subject multimodal emotion recognition hinges on maintaining the consistency of the joint data distribution across individuals. However, real-world deployment frequently triggers the \emph{asymmetric joint distribution collapse}: EEG signals suffer from severe cross-subject distribution shifts, while eye movements sensors are susceptible to packet loss and tracking failures. Existing methods treat domain adaptation and missing-modality imputation as disjoint tasks. Consequently, they fail to resolve the compounded errors when both degradations co-occur, either propagating domain shifts through imputed signals or destroying the joint decision boundary. To tackle this unified challenge, we propose GUARD (\textbf{G}radient-guided \textbf{U}nsupervised \textbf{A}symmetric \textbf{R}ecovery of \textbf{D}istributions). First, GUARD establishes a reliable anchor manifold in the source domain by employing a theoretically grounded gradient-weighted objective, which forces the robust EEG modality to preemptively entangle task-discriminative ocular features. Next, to structurally recover the collapsed joint distribution, we constrain a generative module with downstream perceptual losses, prioritizing emotion-discriminative semantics over mere signal fidelity. Finally, we formulate target-domain adaptation as an ill-posed inverse problem. By driving a cycle-consistent flow, we achieve unsupervised calibration of the recovered joint distribution directly on the target-domain manifold. Extensive experiments demonstrate that GUARD significantly outperforms state-of-the-art methods, maintaining resilient discriminative performance even under complete auxiliary modality failure. Our code and models are made publicly available to ensure complete reproducibility.
