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arXiv · 2509.21964

A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband

Fiona Meier·Giusy Spacone·Sebastian Frey·Luca Benini·Andrea Cossettini
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Abstract

We present a wearable, fully-dry, and ultra-low power EMG system for silent speech recognition, integrated into a textile neckband to enable comfortable, non-intrusive use. The system features 14 fully-differential EMG channels and is based on the BioGAP-Ultra platform for ultra-low power (22 mW) biosignal acquisition and wireless transmission. We evaluate its performance on eight speech commands under both vocalized and silent articulation, achieving average classification accuracies of 87$\pm$3% and 68$\pm$3% respectively, with a 5-fold CV approach. To mimic everyday-life conditions, we introduce session-to-session variability by repositioning the neckband between sessions, achieving leave-one-session-out accuracies of 64$\pm$18% and 54$\pm$7% for the vocalized and silent experiments, respectively. These results highlight the robustness of the proposed approach and the promise of energy-efficient silent-speech decoding.

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Fiona Meier, Giusy Spacone, Sebastian Frey, Luca Benini, Andrea Cossettini. 2025-09-26. A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband. https://arxiv.org/abs/2509.21964

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