Wireless Silent Speech Interface Using Multi-Channel Textile EMG Sensors Integrated into Headphones
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915689005056000 |
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| author | Tang, Chenyu Mallah, Josée Kazieczko, Dominika Yi, Wentian Kandukuri, Tharun Reddy Occhipinti, Edoardo Mishra, Bhaskar Mehta, Sunita Occhipinti, Luigi G. |
| author_facet | Tang, Chenyu Mallah, Josée Kazieczko, Dominika Yi, Wentian Kandukuri, Tharun Reddy Occhipinti, Edoardo Mishra, Bhaskar Mehta, Sunita Occhipinti, Luigi G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13921 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Wireless Silent Speech Interface Using Multi-Channel Textile EMG Sensors Integrated into Headphones Tang, Chenyu Mallah, Josée Kazieczko, Dominika Yi, Wentian Kandukuri, Tharun Reddy Occhipinti, Edoardo Mishra, Bhaskar Mehta, Sunita Occhipinti, Luigi G. Human-Computer Interaction Signal Processing 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. |
| title | Wireless Silent Speech Interface Using Multi-Channel Textile EMG Sensors Integrated into Headphones |
| topic | Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2504.13921 |