Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization
arXiv:2510.20853v2 Announce Type: replace-cross 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
Advances in wearable technology and AI are converging, enabling the collection and interpretation of complex biological signals outside of traditional clinical settings, pushing the frontier of human-computer interaction.
This development allows for ubiquitous and passive monitoring of human physiology through common devices like earphones, offering a rich new data stream for health, performance, and human-computer interfaces without conscious effort.
The ability to generate task-agnostic ExG representations from everyday earphones transforms how physiological data is collected and processed, moving from specialized lab equipment to consumer electronics, expanding access and applications.
- · Wearable tech companies
- · AI/ML research labs
- · Healthcare diagnostics
- · Human-computer interface developers
- · Manufacturers of bulky ExG lab equipment
Ubiquitous, passive physiological monitoring becomes feasible through consumer devices.
Development of personalized AI models that anticipate user needs or health issues based on real-time physiological states accelerates.
Ethical and regulatory frameworks for physiological data privacy become critically important as this data becomes widely accessible and interpretable.
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