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.

处理静态面部表情（FEs）的神经认知机制已得到充分证明，而感知动态面孔的一种基础尚不清楚。在这项研究中，进行了傅里叶变换和脑电图（EEG）数据的时频分析，以检测动态或静态FEs背后的大脑激活情况，同时有21位参与者正在观察以10 Hz频率拍动的动态或静态面孔。特别是，通过脑电图记录的频谱功率分析对稳态视觉诱发电位（SSVEP）进行了量化。此外，还进行了格兰杰因果关系（GC）分析（GCA）来捕获情绪动态或静态FE期间的因果皮层网络动态。发现动态FE（从神经到快乐（N2H），反之亦然（H2N））引起的SSVEP比静态FESEP大。此外，GCA证明，H2N案例中，快乐的FE逐渐变成中性，在后期处理阶段显示出比早期更大的GC度量。因此，增强的SSVEP和动态FE的有效大脑连接性说明，参与者可能需要消耗更多的注意力资源来处理动态面孔，尤其是从快乐面孔转变为中性面孔时。新的神经指数可能有助于我们更好地了解动态和静态有限元的认知过程。增强的SSVEP和动态FE的有效大脑连接性说明，参与者可能需要消耗更多的注意力资源来处理动态面孔，尤其是从快乐面孔转变为中性面孔时。新的神经指数可能有助于我们更好地了解动态和静态有限元的认知过程。增强的SSVEP和动态FE的有效大脑连接性说明，参与者可能需要消耗更多的注意力资源来处理动态面孔，尤其是从快乐面孔转变为中性面孔时。新的神经指数可能有助于我们更好地了解动态和静态有限元的认知过程。