Representation of the brain network by electroencephalograms during facial expressions

Abstract Background Facial expressions, such as smiling and anger, cause many physical and psychological effects in the body, known as ‘embodied emotions’ or ‘facial feedback theory.’ In the clinical application of this theory in certain diseases, such as autism and depression, treatments such as forcing patients to smile have been used. However, the neural mechanisms underlying the representation of facial expressions remain unclear. New method We proposed a method to construct brain networks based on the time course of the synchronization likelihood and determine the effects of various facial expressions on the situation using visual stimulus of faces. This method was applied to analyze electroencephalographic (EEG) data recorded during the recognition and representation of various positive and negative facial expressions. The brain networks were constructed based on the EEG data recorded in 11 healthy participants. Results Channel sets from brain networks during unsymmetrical smiling expressions (i.e., only the right or left side) were highly linearly symmetrical. Channel sets from brain networks during negative facial expressions (i.e., anger and sadness) and symmetrical smiling expressions (i.e., smiling with an opened or closed mouth) were similar. Comparison with existing methods While we obtained brain networks based on time course EEG correlations throughout the experiment, existing methods can analyze EEG data only at a certain time point. Conclusions The comparisons of different facial expressions could be used to identify the side of the facial muscles used while smiling and to determine how similar brain networks are induced by positive and negative facial expressions.