AI 中文总结
针对跨主体脑电情感识别中标签噪声问题，提出PhyDA框架，通过生理噪声量化器和数据自适应标签细化器，统一神经生理先验与数据驱动的标签细化，实验证明该方法显著优于基线，具有可解释性和鲁棒性。
AI 中文摘要
基于脑电图（EEG）的情感识别能高精度捕捉情感神经信号，但跨主体变异性和标签噪声是其实际医疗应用的关键挑战。现有标签去噪方法缺乏生理基础，而基于生理信息的方法依赖手工超参数。为此提出PhyDA框架，它由生理噪声量化器（PhyNQ）和数据自适应标签细化器（DALR）组成，能统一神经生理先验与数据驱动的标签细化。在三个公共数据集上的实验表明，PhyDA显著优于基线方法，可视化也证实了其神经生理可解释性和实际鲁棒性。
英文摘要
Electroencephalography (EEG)-based emotion recognition captures affective neural signals with high temporal precision, but cross-subject variability and label noise remain critical challenges to its practical healthcare deployment. Existing label-denoising methods lack physiological grounding, while physiology-informed approaches rely on hand-crafted hyperparameters. To bridge these two paradigms, we propose PhyDA, a plug-and-play, tuning-free framework that unifies neurophysiological priors with data-driven label refinement. PhyDA comprises two modules. Since cross-subject variability renders global thresholds suboptimal, the Physiological Noise Quantifier (PhyNQ) exploits a spectral slope} to produce a subject-specific noise score, providing a neurophysiologically interpretable quality assessment {that naturally adapts to each individual. The Data-Adaptive Label Refiner (DALR) directly adopts this noise score as the contamination ratio to drive a label refinement pipeline that requires no additional neural network training, thereby directly mitigating the impact of inter-subject label noise. Extensive experiments on three public datasets (DEAP, SEED, SEED-IV) across seven backbone architectures under strict leave-one-subject-out cross-validation demonstrate that PhyDA consistently and significantly outperforms both general and EEG-tailored label-denoising baselines, achieving average accuracy gains of 2.76%, 2.66%, and 3.32%, respectively. Visualization further confirms its neurophysiological interpretability and practical robustness. The source code is available at: https://github.com/HongyuZhu-s/PhyDA.
CommentsSubmitted; 11 pages
