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Updated: Jul 25, 2026
Extraction of the EPP Component from the Surface EMG
Published on: December 16, 2009
Predicting 3D lip shapes using facial surface EMG
Merijn Eskes1,2, Maarten J A van Alphen1, Alfons J M Balm1,3
1Dept of Head and Neck Oncology and Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands.
Aim:
The aim of this study is to prove that facial surface electromyography (sEMG) conveys sufficient information to predict 3D lip shapes. High sEMG predictive accuracy implies we could train a neural control model for activation of biomechanical models by simultaneously recording sEMG signals and their associated motions.
Materials And Methods:
With a stereo camera set-up, we recorded 3D lip shapes and simultaneously performed sEMG measurements of the facial muscles, applying principal component analysis (PCA) and a modified general regression neural network (GRNN) to link the sEMG measurements to 3D lip shapes. To test reproducibility, we conducted our experiment on five volunteers, evaluating several sEMG features and window lengths in unipolar and bipolar configurations in search of the optimal settings for facial sEMG.
Conclusions:
The errors of the two methods were comparable. We managed to predict 3D lip shapes with a mean accuracy of 2.76 mm when using the PCA method and 2.78 mm when using modified GRNN. Whereas performance improved with shorter window lengths, feature type and configuration had little influence.
