{"ID":2865096,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21964","arxiv_id":"2509.21964","title":"A Parallel Ultra-Low Power Silent Speech Interface based on a Wearable, Fully-dry EMG Neckband","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.21964","url_pdf":"https://arxiv.org/pdf/2509.21964v1","authors":"[\"Fiona Meier\",\"Giusy Spacone\",\"Sebastian Frey\",\"Luca Benini\",\"Andrea Cossettini\"]","published":"2025-09-26T06:52:34Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.SD\",\"eess.AS\",\"eess.SP\"]","methods":"[]","has_code":false}