Electrophysiological evidence for inhibition hypothesis of micro-expressions based on tensor component analysis and Physarum network algorithm.

The inhibition hypothesis advocated by Ekman (1985) states when an emotion is concealed or masked, the true emotion is manifested as a micro-expression (ME) which is a fleeting expression lasting for 40 to 500 ms. However, research about the inhibition hypothesis of ME from the perspective of electrophysiology is lacking. Here, we report the electrophysiological evidence obtained from an electroencephalography (EEG) data analysis method. Specifically, we designed an ME elicitation paradigm to collect data of MEs of positive emotions and EEG from 70 subjects, and proposed a method based on tensor component analysis (TCA) combined with the Physarum network (PN) algorithm to characterize the spatial, temporal, and spectral signatures of dynamic EEG data of MEs. The proposed TCA-PN methods revealed two pathways involving dorsal and ventral streams in functional brain networks of MEs, which reflected the inhibition processing and emotion arousal of MEs. The results provide evidence for the inhibition hypothesis from an electrophysiological standpoint, which allows us to better understand the neural mechanism of MEs.