Objectives: Surface electromyography (sEMG) is a standard tool in clinical routine and clinical or psychosocial experiments also including speech research and orthodontics to measure the activity of selected facial muscles to objectify facial movements during specific facial exercises or experiments with emotional expressions. Such muscle-specific approaches neglect that facial muscles act more as an interconnected network than as single facial muscles for specific movements. What is missing is an optimal sEMG setting allowing a synchronous measurement of the activity of all facial muscles as a whole.
Methods: A total of 36 healthy adult participants (53% women, 18–67 years) were included. Electromyograms were recorded from both sides of the face using an arrangement of electrodes oriented by the underlying topography of the facial muscles (Fridlund scheme) and simultaneously by a geometric and symmetrical arrangement on the face (Kuramoto scheme). The participants performed a standard set of different facial movement tasks. Linear mixed-effects models and adjustment for multiple comparisons were used to evaluate differences between the facial movement tasks, separately for both applied schemes. Data analysis utilized sEMG amplitudes and also their maximum-normalized values to account for amplitude differences between the different facial movements.
Results: Surface electromyography activation characteristics showed systematic regional distribution patterns of facial muscle activation for both schemes with very low interindividual variability. The statistical significance to discriminate between the different sEMG patterns was good for both schemes (significant comparisons for sEMG amplitudes: 87.3%, both schemes, normalized values: 90.9%, Fridlund scheme, 94.5% Kuramoto scheme), but the Kuramoto scheme performed considerably superior.
Conclusion: Facial movement tasks evoke specific patterns in the complex network of facial muscles rather than activating single muscles. A geometric and symmetrical sEMG recording from the entire face seems to allow more specific detection of facial muscle activity patterns during facial movement tasks. Such sEMG patterns should be explored in more clinical and psychological experiments in the future.