基于双层自监督持续学习的鲁棒情绪识别
Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learning
AI总结：
针对生理信号情绪识别中跨个体差异、标签噪声及无标签连续流式数据的挑战，提出基于动态记忆缓冲区的双层自监督持续学习框架SSOCL，经实验验证其跨被试泛化性与流数据适应能力优于现有方法。
摘要：
通过脑电图（EEG）等生理信号进行情绪识别已成为情感计算的重要方向，为捕捉人类情绪提供了客观途径。然而，生理数据存在跨受试者差异性和标签噪声的特点，制约了情绪识别模型的性能。现有域适应和持续学习方法难以解决这些问题，尤其是在数据持续流式输入且无标签的真实场景下。为突破这些局限，我们提出一种基于动态记忆缓冲区的新型双层自监督持续学习框架SSOCL。该双层架构迭代优化动态缓冲区与伪标签分配，有效保留代表性样本，实现从连续无标签生理数据流到情绪识别的泛化，分配得到的伪标签随后用于精准情绪预测。框架的核心组件包括快速适应模块和聚类映射模块，可实现鲁棒学习并有效应对不断演化的数据流。在两项主流EEG任务上的实验验证表明，该框架能够适应连续数据流，同时保持良好的跨受试者泛化能力，性能优于现有方法。
英文摘要：
Emotion recognition through physiological signals such as electroencephalogram (EEG) has become an essential aspect of affective computing and provides an objective way to capture human emotions. However, physiological data characterized by cross-subject variability and noisy labels hinder the performance of emotion recognition models. Existing domain adaptation and continual learning methods struggle to address these issues, especially under realistic conditions where data is continuously streamed and unlabeled. To overcome these limitations, we propose a novel bi-level self-supervised continual learning framework, SSOCL, based on a dynamic memory buffer. This bi-level architecture iteratively refines the dynamic buffer and pseudo-label assignments to effectively retain representative samples, enabling generalization from continuous, unlabeled physiological data streams for emotion recognition. The assigned pseudo-labels are subsequently leveraged for accurate emotion prediction. Key components of the framework, including a fast adaptation module and a cluster-mapping module, enable robust learning and effective handling of evolving data streams. Experimental validation on two mainstream EEG tasks demonstrates the framework's ability to adapt to continuous data streams while maintaining strong generalization across subjects, outperforming existing approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
