Tracing The Time Course: Brain Mechanism Of Facial Emotion Classification

Emotion is a very important aspect of social communication in our life,so the abilities to identify people's emotion,discriminate other's mood and make appropriate responses are vital social skills for people,thus the research about facial emotion classification is an essential part in human brain investigation.This study has mainly focused on the time course of ficial expression classification,and paid more attention to the identification of positive classification advantage(PCA).Although the neutral face classification is faster than the happy and sad faces,the very obviously happy face classification advantage,that is,happy face claasification is significantly faster than the sad faces,is an interesting phenomenon in psychological studies.In this study,through EEG technology,we conducted an experiment with three basic facial expressions as stimuli.Each time when different expressions appear,participants should make a judge of the emotion and press the key reaction as soon as possible.EEG data from 18 normal subjects was collected by 64 EEG systems(NeuroScan),and after preprocess including signal denoising,baseline correction,epoch extraction and basic filtering,the EEG data was conducted wavelet analysis to obtain spectral energy as feature.Then the behavior data,event related potentials(ERPs)data and spectral energy were carried out statistical analysis,and finally the time course of facial expression classification was traced and PCA phenomenon was positioned on the time domain.From the ERP results,it can be found,compared with the sad face,happy face could elicit smaller N2 and larger N170 component.The P3 component is likely to modulate the PCA phenomenon with higher amplitute and shorter latency.And,strong correlation between the reaction time and amplitude and latency of the P3 component also revealed that the P3 component plays a certain role in the facial expression classification process and the phenomenon of PCA.Therefore,it can be drawn that although the processing of facial expression classification may emerge from time of N170 component,the robust happy face categorization advantage was modulated by late components but not early ones.On the other hand,it can be learned from the brain rhythms that this process of facial expression classification linked to theta,alpha and beta oscillations and this classification perfomanced from 100 ms after stimuli onset.Intersetingly,there was strong negative correlations between theta oscillation and reaction times 200 ms post-stimuli,which could be thought as the latency of P300.In line with the interaction effect of P300 and theta oscillation,it could be speculated that theta band may participate in the modulation of happy faces categorization advantage,and this modulation effect existed during the later processing,thus,it can be drawn that though multiple brain rhythms may relate to the facial expression classification,the PCA phenomenon was likely to be modulated by theta oscillation.