Facial muscles are uniquely attached to the skin,densely innervated,and exhibit complex coactivation patterns enabling fine motor control.Facial surface Electromyography(sEMG)effectively assesses muscle function,yet t...Facial muscles are uniquely attached to the skin,densely innervated,and exhibit complex coactivation patterns enabling fine motor control.Facial surface Electromyography(sEMG)effectively assesses muscle function,yet traditional setups require precise electrode placement and limit mobility due to mechanical artifacts.Signal extraction is hindered by noise and cross-talk from adjacent muscles,making it challenging to associate facial muscle activity with expressions.We leverage a novel 16-channel conformal sEMG system to extract meaningful electrophysiological data from 32 healthy individuals.By applying denoising and source separation techniques,we extracted independent components,clustered them spatially,and built a facial muscle atlas.Furthermore,we established a functional mapping between these clusters and specific muscle units,providing a framework for understanding facial muscle activation.Using this foundation,we demonstrated a deep-learning model to predict facial expressions.This approach enables precise,participant-specific monitoring with applications in medical rehabilitation and psychological research.展开更多
基金support from the Deutsche Forschungsgemeinschaft(DFG),Grant No.GU-463/12-1support by the Israel Science Foundation(ISF)Grant No.1355/17,and the European Research Council(ERC),Grant Outer-Ret—101053186support of the Tel Aviv University Center for AI&Data Science。
文摘Facial muscles are uniquely attached to the skin,densely innervated,and exhibit complex coactivation patterns enabling fine motor control.Facial surface Electromyography(sEMG)effectively assesses muscle function,yet traditional setups require precise electrode placement and limit mobility due to mechanical artifacts.Signal extraction is hindered by noise and cross-talk from adjacent muscles,making it challenging to associate facial muscle activity with expressions.We leverage a novel 16-channel conformal sEMG system to extract meaningful electrophysiological data from 32 healthy individuals.By applying denoising and source separation techniques,we extracted independent components,clustered them spatially,and built a facial muscle atlas.Furthermore,we established a functional mapping between these clusters and specific muscle units,providing a framework for understanding facial muscle activation.Using this foundation,we demonstrated a deep-learning model to predict facial expressions.This approach enables precise,participant-specific monitoring with applications in medical rehabilitation and psychological research.
