Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyday tasks remains challenging due to two key limitations: (i)~insufficient data diversity, as most ExG recordings are collected in controlled labs with bulky, expensive devices; and (ii)~task-specific model designs that require tailored processing (i.e., targeted frequency filters) and architectures, which limit generalization across tasks. To address these challenges, we introduce an approach for scalable, task-agnostic ExG monitoring in the wild. We collected 50 hours of unobtrusive free-living ExG data with an earphone-based hardware prototype to narrow the data diversity gap. At the core of our approach is Physiology-informed Multi-band Tokenization (PiMT), which decomposes ExG signals into 12 physiology-informed tokens, followed by a reconstruction task to learn robust representations. This enables adaptive feature recognition across the full frequency spectrum while capturing task-relevant information. Experiments on our new DailySense dataset, the first to enable ExG-based analysis across five human senses, together with four public ExG benchmarks, demonstrate that PiMT consistently outperforms state-of-the-art methods across diverse tasks.

翻译：电生理（ExG）信号为人类生理状态提供了宝贵洞察，然而，构建能泛化到日常任务的基座模型仍面临两大关键限制：（i）数据多样性不足——多数ExG记录均在受控实验室环境中采用笨重且昂贵的设备采集；（ii）任务特定模型设计——需定制化处理（如目标频率滤波器）与架构，限制了跨任务泛化能力。针对这些挑战，我们提出一种可在现实场景中实现可扩展、任务无关ExG监测的方法。通过基于耳机的硬件原型，我们采集了50小时无干扰的自由生活ExG数据，以弥合数据多样性缺口。该方法的核心是生理知识启发的多频带分词技术（PiMT），该技术将ExG信号分解为12个生理知识驱动的令牌，并通过重建任务学习鲁棒表征。这使得模型能够在全频谱范围内实现自适应特征识别，同时捕捉任务相关信息。基于我们新构建的DailySense数据集（首个支持跨五种人类感官进行ExG分析的数据集）与四个公开ExG基准的实验表明，PiMT在不同任务中均持续优于现有最先进方法。