用Transformer模型无监督分解皮肤电反应，提升真实场景下心理状态分析精度。
Transformer-Based Decomposition of Electrodermal Activity for Real-World Mental Health Applications
- 基于Autoformer改进的Feel Transformer，通过池化与趋势移除实现无监督分解。
- 在真实穿戴设备数据上，保持生理信号特征精度且抗噪性强。
- 适合用于实时压力预测与数字心理健康干预系统开发。
将皮肤电活动（EDA）分解为短时的瞬态反应（phasic）和长时基线变化（tonic）是提取情绪与生理生物标志物的关键。本研究对比了基于知识、统计及深度学习的多种EDA信号分解方法，重点针对可穿戴设备采集的真实环境数据。作者提出新型的Feel Transformer模型，基于Autoformer架构，无需显式标注即可分离出phasic与tonic成分。该模型结合池化与趋势移除机制，确保分解结果符合生理学意义。与Ledalab、cvxEDA及传统去趋势法相比，Feel Transformer在保持皮电反应频率、幅度及基线斜率等特征保真度的同时，展现出更强的噪声鲁棒性。实验表明其具备实时生物信号分析潜力，未来可用于压力预测、数字心理健康干预与生理趋势预测。
原文摘要 · Abstract (English)
Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting.
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