Repeatability of facial electromyography (EMG) activity over corrugator supercilii and zygomaticus major on differentiating various emotions
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Recent affective computing findings indicated that effectively identifying users’ emotional responses is an important issue to improve the quality of ambient intelligence. In the current study, two bipolar facial electromyography (EMG) channels over corrugator supercilii and zygomaticus major were employed for differentiating various emotional states in two dimensions of valence (negative, neutral and positive) and arousal (high and low) while participants looked at affective visual stimuli. The results demonstrated that corrugator EMG and zygomaticus EMG efficiently differentiated negative and positive emotions from others, respectively. Moreover, corrugator EMG discriminated emotions on valence clearly, whereas zygomaticus EMG was ambiguous in neutral and negative emotional states. However, there was no significant statistical evidence for the discrimination of facial EMG responses in the dimension of arousal. Furthermore, correlation analysis proved significant correlations between facial EMG activities and ratings of valence performed by participants and other samples, which strongly supported the consistency of facial EMG reactions and subjective emotional experiences. In addition, the repeatability of facial EMG indicated by intraclass correlation coefficient (ICC) were provided, in which corrugator EMG held an excellent level of repeatability, and zygomaticus EMG grasped only a poor level of repeatability. Considering these results, facial EMG is reliable and effective to identify negative and positive emotional experiences elicited by affective visual stimuli, which may offer us an alternative method in building a basis for automated classification of users’ affective states in various situations.
KeywordsRepeatability Facial electromyography Emotion Affective visual stimuli Valence
This research was supported by grants from the Transregional Collaborative Research Center SFB/TRR 62 “Companion-Technology for Cognitive Technical System” funded by the German Research Foundation (DFG) and a doctoral scholarship by the China Scholarship Council (CSC) for Jun-Wen Tan.
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