AI 中文总结
本研究提出gmsEDA，一种基于广义矩阵分离的EDA信号分解方法，联合分析多组记录以应对噪声与运动伪影，经实验验证其性能优于现有标准工具。
AI 中文摘要
皮肤电活动（EDA）信号通过皮肤电导变化反映交感神经系统的唤醒状态，广泛应用于心理学与行为学研究。将观测到的EDA信号分解为缓慢变化的紧张性基线和刺激驱动的相位成分是重要预处理步骤，但现有方法单独处理信号，对噪声和运动伪影高度敏感。本研究提出gmsEDA，一种基于广义矩阵分离的新型分解方法，其模型设计用于应对噪声与运动伪影。该方法联合分析多组记录而非单次处理单个信号，利用跨信号共享模式以生成更准确、鲁棒的结果。对模拟数据和真实数据的数值实验表明，此方法优于现有标准工具。
英文摘要
Electrodermal activity (EDA) signals, which reflect sympathetic nervous system arousal through changes in skin conductance, are widely used in psychological and behavioral research. Decomposing an observed EDA signal into its slowly varying tonic baseline and stimulus-driven phasic component is an important preprocessing step; however, existing methods process signals in isolation and remain highly sensitive to noise and motion artifacts. This work introduces gmsEDA, a new decomposition method based on generalized matrix separation whose model is designed to cope with noise and motion artifacts. Our method analyzes multiple recordings jointly rather than one at a time, taking advantage of patterns shared across signals to produce more accurate and robust results. Numerical experiments on both simulated and real data shows that this approach outperforms existing standard tools.
Comments25 pages
