Wireless Silent Speech Interface Using Multichannel Textile EMG Sensors Integrated Into Headphones

Abstract

This article presents a novel wireless silent speech interface (SSI) integrating multichannel textile-based electromyography (EMG) electrodes into headphone earmuff for real-time, hands-free communication. Unlike conventional patch-based EMG systems, which require large-area electrodes on the face or neck, our approach ensures comfort, discretion, and wearability while maintaining robust silent speech decoding. The system utilizes four graphene/PEDOT:PSS-coated textile electrodes to capture speech-related neuromuscular activity, with signals processed via a compact ESP32-S3-based wireless readout module. To address the challenge of variable skin-electrode coupling, we propose a 1-D SE-ResNet architecture incorporating squeeze-and-excitation (SE) blocks to dynamically adjust per-channel attention weights, enhancing robustness against motion-induced impedance variations. The proposed system achieves 96% accuracy on ten commonly used voice-free control words, outperforming conventional single-channel and nonadaptive baselines. Experimental validation, including explainable AI (XAI)-based attention analysis and t-SNE feature visualization, confirms the adaptive channel selection capability and effective feature extraction of the model. This work advances wearable EMG-based SSIs, demonstrating a scalable, low-power, and user-friendly platform for silent communication, assistive technologies, and human–computer interaction.