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原文摘要

Foundation models have recently extended beyond natural language and vision to time-series domains, including physiological signals. Yet developing foundation models for electrodermal activity (EDA) remains hindered by the absence of large-scale, curated, openly accessible datasets. EDA reflects sympathetic activity and is widely used to infer cognitive load, stress, and engagement. However, few wearable devices feature EDA sensing, and the only large-scale EDA archive to date is proprietary. To address this gap, we compile EDAMAME, a collection of EDA traces from 29 public datasets comprising approximately 23,000 hours of data from 731 users. Using this collection, we train UME, the first foundation model for EDA. As a frozen feature extractor, UME outperforms generic handcrafted features and matches generalist time-series foundation models while requiring 20x fewer computational resources; fine-tuning on the downstream tasks further yields additional performance gains in most experiments. UME also generalizes to EDA from two devices unseen during training. We further show that UME's performance improves as the amount of pretraining data grows, underscoring the importance of large-scale data for effective foundation models. Our findings highlight intrinsic challenges of EDA signals, revealing future research opportunities. We share EDAMAME under tiered access and also release model weights and code.