{"ID":2851511,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20853","arxiv_id":"2510.20853","title":"Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization","abstract":"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.","short_abstract":"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; a...","url_abs":"https://arxiv.org/abs/2510.20853","url_pdf":"https://arxiv.org/pdf/2510.20853v2","authors":"[\"Hyungjun Yoon\",\"Seungjoo Lee\",\"Yu Yvonne Wu\",\"Xiaomeng Chen\",\"Taiting Lu\",\"Freddy Yifei Liu\",\"Taeckyung Lee\",\"Hyeongheon Cha\",\"Haochen Zhao\",\"Gaoteng Zhao\",\"Dongyao Chen\",\"Cecilia Mascolo\",\"Sung-Ju Lee\",\"Lili Qiu\"]","published":"2025-10-22T05:11:02Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.CL\",\"cs.SD\"]","methods":"[]","has_code":false}