EEG Decoding of Dynamic Facial Expressions of Emotion: Evidence from SSVEP and Causal Cortical Network Dynamics.

The neural cognitive mechanism in processing static facial expressions (FEs) has been well documented, whereas the one underlying perceiving dynamic faces remains unclear. In this study, Fourier transformation and time-frequency analysis of Electroencephalography (EEG) data were carried out to detect the brain activation underlying dynamic or static FEs while twenty-one participants were viewing dynamic or static faces flicking at 10 Hz. In particular, steady-state visual evoked potentials (SSVEPs) were quantified through spectral power analysis of EEG recordings. Besides, Granger causality (GC) analysis (GCA) was also performed to capture the causal cortical network dynamics during dynamic or static FEs of emotion. It was discovered that the dynamic (from neural to happy (N2H) or vice versa (H2N)) FEs elicited larger SSVEPs than the static ones. Additionally, GCA demonstrated that the H2N case, in which happy FEs were being gradually changed into neutral ones, exhibited larger GC measure during the late processing stage than that from the early stage. Consequently, enhanced SSVEPs and effective brain connectivity for dynamic FEs illustrated that participants might need consume more attentional resources to process the dynamic faces, particularly for the change from happy to neutral faces. The new neural index might facilitate us to better understand the cognitive processing of dynamic and static FEs.