An in-sensor communication electronic textile for imperceptible and ultrarobust silent speech

Aphasiacs with impaired vocal organs suffer profound communication barriers, making silent speech decoding essential. Although audible speech is absent, subtle multi-muscle activities are retained. This provides a physiological basis for the sensing modality that reliably decodes silent speech. Yet conventional microphone and piezoelectric films remain susceptible to dispersion–fidelity conflict, external disturbance in the complex environments, and are further constrained by sensing–communication separation. Here, we report an in-sensor communication scarf interface that imperceptibly captures multi-muscle activities and ultrarobustly decodes silent speech. By fusing spoof surface plasmon–driven in-sensor electromagnetic physiological communication with metamaterial topological embroidery, the system establishes exquisite reconciliation between dispersion-regulated spatial selectivity and ultrahigh fidelity (7 fs²), enabling phase-resolved differentiation and precise capture of multi-muscle activities. This minimalist yet informative rich component design radically transcends limitations of sensing–communication separation, microphone, and piezoelectric films, and greatly delivers anti-motion artifact by 24,300%, anti-electromagnetic interference by 46.4 dB, and anti-noise by 33.44 dB in the complex environments. Combining with machine learning, the interface achieves 98.7% recognition and supports assistive interaction and sleep apnea monitoring. The interface bridges metamaterial electromagnetics and human electrophysiology, establishing a transformative in-sensor physiological decoding paradigm for electromagnetic physiological communication and adaptive myogenic interfacing. Conventional silent speech decoding devices are limited by dispersion–fidelity conflict, external disturbance, and sensing–communication separation. Here, the authors present an in-sensor communication scarf that captures multi-muscle activities to decode silent speech for speech recognition and assistive interaction applications.