Abstract

This paper presents a novel wireless silent speech interface (SSI) integrating multi-channel textile-based 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 1D 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 10 commonly used voice-free control words, outperforming conventional single-channel and non-adaptive baselines. Experimental validation, including 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.

Abstract

This paper presents a novel wireless silent speech interface (SSI) integrating multi-channel textile-based 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 1D 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 10 commonly used voice-free control words, outperforming conventional single-channel and non-adaptive baselines. Experimental validation, including 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.

Abstract

This paper presents a novel wireless silent speech interface (SSI) integrating multi-channel textile-based 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 1D 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 10 commonly used voice-free control words, outperforming conventional single-channel and non-adaptive baselines. Experimental validation, including 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.

Abstract

This paper presents a novel wireless silent speech interface (SSI) integrating multi-channel textile-based 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 1D 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 10 commonly used voice-free control words, outperforming conventional single-channel and non-adaptive baselines. Experimental validation, including 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.