用耳塞采集生理信号，通过12个生理分段的重构学习通用表征。
Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization
- 将脑电等生理信号按12个生理频段分块，用重建任务学表征。
- 在5种感官数据上表现优于现有方法，跨任务泛化强。
- 适合做可穿戴设备上的通用生理监测，尤其关注日常场景。
脑电等生理信号（ExG）蕴含丰富人体信息，但构建能跨日常任务通用的基础模型仍面临两大挑战：(i) 数据多样性不足，因多数记录依赖笨重昂贵的实验室设备；(ii) 模型设计任务特异，需定制滤波和结构，限制泛化能力。为此，本文提出一种可扩展的、任务无关的野外生理监测方法。我们通过耳塞式硬件原型采集了50小时无感自由生活数据，缩小数据多样性差距。核心是生理启发的多频段分块（PiMT），将ExG信号分解为12个生理相关分块，再通过重建任务学习鲁棒表征，实现全频谱自适应特征识别并保留任务相关信息。在首个支持五种感官分析的每日感知（DailySense）数据集及四个公开基准上实验表明，PiMT在多种任务中持续超越当前最优方法。
原文摘要 · Abstract (English)
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.
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