通过自适应加权电极与半监督域适应，提升少量标注下跨被试情绪识别精度。
PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition
- 用电极重要性加权和伪标签优化，减少冗余与个体差异影响。
- 在3个数据集上优于现有方法，仅用少量标注实现稳定跨被试识别。
- 适合标注稀缺但需跨被试通用的情绪计算场景。
脑电图（EEG）以高时间分辨率捕捉大脑内源活动，在精确情绪解码方面具有巨大潜力。然而，电极通道冗余与显著的跨被试差异仍是实现可扩展泛化的关键障碍。为解决这些问题，本文提出一种名为PRISM（Prioritized Channel Importance with Semi-supervised Domain Adaptation）的新框架，支持标签高效跨被试情绪识别。在通道层面，PRISM通过轻量级专家集成分配可微、数据依赖的通道权重，增强可靠电极信号并抑制干扰项；在域层面，利用置信度筛选的伪标签驱动一致性正则化与域对齐，缓解个体特异性异质性。大量实验表明，PRISM在DEAP、DREAMER和SEED数据集上均超越当前最优方法，在有限标注条件下实现鲁棒的跨被试泛化能力。
原文摘要 · Abstract (English)
Electroencephalogram (EEG) captures endogenous brain activity with high temporal fidelity and holds substantial promise for precise emotion decoding. However, channel redundancy and pronounced inter-subject variability remain key obstacles to scalable generalization. To address these limitations, we propose a novel framework termed PRioritized channel Importance with Semi-supervised doMain adaptation (PRISM), enabling label-efficient cross-subject emotion decoding. On the channel side, PRISM assigns differentiable, data-dependent channel weights via a lightweight expert ensemble, amplifying reliable electrodes while suppressing distractors. On the domain side, PRISM leverages unlabeled data through confidence-filtered pseudo-labels to drive consistency regularization and domain alignment, mitigating subject-specific heterogeneity. Extensive experiments show that PRISM surpasses state-of-the-art methods on DEAP, DREAMER, and SEED datasets, achieving robust cross-subject generalization given limited annotations.
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