Repeatability of facial electromyography (EMG) activity over corrugator supercilii and zygomaticus major on differentiating various emotions
- 737 Downloads
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.
- Bradley MM, Lang PJ (2008) International affective picture system (IAPS) in the study of emotion and attention. In Coan JA and Allen JJ (Eds), Handbook of emotion elicitation and assessment. Oxford university press, pp 29–46Google Scholar
- Ekman P, Friesen WV (1975) Unmasking the face: a guide to recognizing emotions from facial clues. Prentice-Hall, Englewood CliffsGoogle Scholar
- Frantzidis CA, Bratsas C, Klados MA, Konstantinidis E, Lithari CD, Vivas AB, Papadelis CL, Kaldoudi E, Pappas C, Bamidis PD (2010) On the classification of emotional biosignals evoked while viewing affective pictures: an integrated data-mining-based approach for healthcare applications. IEEE Trans Inf Technol Biomed 14(2):309–318CrossRefGoogle Scholar
- Lang PJ, Bradley MM, Cuthbert BN (2008) International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical Report A-8. University of Florida, GainesvilleGoogle Scholar
- Mehrabian A (1995) Framework for a comprehensive description and measurement of emotional states. Genet Soc Gen Psychol 121(3):339–361Google Scholar
- Tassinary LG, Cacioppo JT, Vanman EJ (2007) The skeleto-motor system: surface electromyography. In: Cacioppo JT, Tassinary LG, Berntson GG (eds) Handbook of psychophysiology, 3rd edn. Cambridge University Press, New York, pp 267–302Google Scholar
- Unz DC, Schwab F (2005) Viewers viewed: facial expression patternswhile watching TV news. In: Anolli L, JR Duncan S, Riva G (eds) The hidden structure of interaction: from neurons to culture patterns. IOS press, Amsterdam, pp 253–264Google Scholar
- Van Boxtel A (2010) Facial EMG as a tool for inferring affective states. In: Proceedings of measuring behavior 2010, Eindhoven, the Netherlands, August 24–27, 2010Google Scholar
- Walter S, Kessler H, Gruss S, Jerg-Bretzke L, Scheck A, Stroebel J, Hoffmann H, Traue HC (2011) The influence of neuroticism and psychological symptoms on the assessment of images in three dimensional emotion space. GMS Psychosoc Med 8:Doc04 (20060606)Google Scholar
- Wolak ME, Fairbairn DJ, Paulsen YR (2011) Guidelines for estimating repeatability. Methods Ecol Evol. doi 10.1111/j.2041-210X.2011.00124.x