Facial electromyography

Facial electromyography (fEMG) refers to an electromyography (EMG) technique that measures muscle activity by detecting and amplifying the tiny electrical impulses that are generated by muscle fibers when they contract.

Facial electromyography
Corrugator supercilii muscle (associated with frowning)
Purposemeasures muscle activity by detecting electrical impulses(face)

It primarily focuses on two major muscle groups in the face, the corrugator supercilii group which is associated with frowning and the zygomaticus major muscle group which is associated with smiling.[1][2]

Uses
Zygomaticus major muscle (associated with smiling)

Facial EMG has been studied to assess its utility as a tool for measuring emotional reaction.[3] Studies have found that activity of the corrugator muscle, which lowers the eyebrow and is involved in producing frowns, varies inversely with the emotional valence of presented stimuli and reports of mood state. Activity of the zygomatic major muscle, which controls smiling, is said to be positively associated with positive emotional stimuli and positive mood state.

Facial EMG has been used as a technique to distinguish and track positive and negative emotional reactions to a stimulus as they occur.[4] A large number of those experiments have been conducted in controlled laboratory environments using a range of stimuli, e.g., still pictures, movie clips and music pieces.

It has also been used to investigate emotional responses in individuals with autism spectrum disorders.[5]

Although commonly used as an index of emotional responses, facial muscle activity is also influenced by the social context in which it is measured.[6] Using facial EMG in immersive virtual environments, Philipp, Storrs, and Vanman showed that even relatively impoverished social cues in a virtual environment can cause increases in zygomaticus major activity that are unrelated to self-reported emotional states.[7]

In 2012 Durso et al. were able to show that facial EMG could be used to detect confusion, both in participants who admitted being confused and in those who did not, suggesting that it could be used as an effective addition to a sensor suite as a monitor of loss of understanding or loss of situation awareness.[8]

Market research and gaming

Two areas where facial EMG techniques have been used are in advertising research[9][10] and in video gaming.[11][12]

  1. Advertising research - Of late, facial EMG has been used to test audience response to commercial advertising. Facial EMG activity measures during a viewing of commercials embedded in TV program clips have been used to describe a commercial's level of emotional activation and engagement. Measurement of the corrugator and zygomatic muscles, yield an overall positive and negative emotional activation score. The moment to moment activation that are recorded are said to measure the dynamic emotional response to a commercial and yield useful insights about the elements of the commercials. Lou et al.[10] showed that facial EMG activity can be used to forecast how game players would be addicted to an online game before the game is officially introduced to the market.
  2. Gaming and Human-Computer Interaction (HCI) - Ravaja,[11] Hazlett[13] and Mandryk[12] used facial EMG techniques to demonstrate that positive and negative emotions can be measured in real time during video game play. The emotional profiling of games give a useful evaluation of a game's impact on a player, how compelling they find the game, how the game measures up to other games in its genre, and how the different elements of the game enhance or detract from the game's approach to engaging the player.[14]

Advantages

Proponents of Facial EMG point to the following advantages:

  1. Facial Electromyography (or fEMG) is a precise and sensitive method to measure emotional expression.
  2. Unlike self-reports, fEMG does not depend upon language and does not require cognitive effort or memory.
  3. fEMG is capable of registering the response even when subjects were instructed to inhibit their emotional expression.
  4. Yields a lot of data and is continuous and scalar (hence more credible.)
  5. It is able to measure facial muscle activities to even weakly evocative emotional stimuli.
  6. Less intrusive than other physiological measures like fMRI and EEG.
  7. Like other physiological measures, facial EMG measurement technique is often the only useful approach when movement is not visible.

Criticisms
  1. The technique is intrusive and may alter natural expression.
  2. The number of muscles it can employ is limited by how many electrodes can be attached to the face.
  3. Certain medicines that act on the nervous system, such as muscle relaxants and anticholinergics, can change electromyography (EMG) results.

See also
  • Facial Action Coding System
  • Computer processing of body language

References
  1. Larsen JT, Norris CJ, Cacioppo JT (September 2003). "Effects of positive and negative affect on electromyographic activity over zygomaticus major and corrugator supercilii". Psychophysiology. 40 (5): 776–85. doi:10.1111/1469-8986.00078. PMID 14696731.
  2. Sato W, Fujimura T, Suzuki N (October 2008). "Enhanced facial EMG activity in response to dynamic facial expressions". Int J Psychophysiol. 70 (1): 70–4. doi:10.1016/j.ijpsycho.2008.06.001. PMID 18598725.
  3. Dimberg U (September 1990). "Facial electromyography and emotional reactions". Psychophysiology. 27 (5): 481–94. doi:10.1111/j.1469-8986.1990.tb01962.x. PMID 2274612.
  4. Wolf K, Mass R, Ingenbleek T, Kiefer F, Naber D, Wiedemann K (October 2005). "The facial pattern of disgust, appetence, excited joy and relaxed joy: an improved facial EMG study". Scand J Psychol. 46 (5): 403–9. doi:10.1111/j.1467-9450.2005.00471.x. PMID 16179022.
  5. Oberman LM, Winkielman P, Ramachandran VS (July 2009). "Slow echo: facial EMG evidence for the delay of spontaneous, but not voluntary, emotional mimicry in children with autism spectrum disorders". Dev Sci. 12 (4): 510–20. doi:10.1111/j.1467-7687.2008.00796.x. PMID 19635079.
  6. Hess, U; Banse, R; Kappas, A (1995). "The intensity of facial expression is determined by underlying affective state and social situation". Journal of Personality and Social Psychology. 69 (2): 280–288. doi:10.1037/0022-3514.69.2.280.
  7. Philipp, MC; Storrs, K; Vanman, E (2012). "Sociality of facial expressions in immersive virtual environments: A facial EMG study". Biological Psychology. 91 (1): 17–21. doi:10.1016/j.biopsycho.2012.05.008. PMID 22652089. S2CID 14744015.
  8. Durso, F.T., Geldbach, K.M. and Cotbalis, P., "Detecting Confusion Using Facial Electromyography", Human Factors, Vol. 54, No. 1, February 2012.
  9. Boll, P.D., Lang, A., Potter, R.F., The Effects of Message Valence and Listener Arousal on Attention, Memory and Facial Muscular Responses to Radio Advertisements. Communication Research, Vol. 28 (2001).
  10. Lou, Jing-Kai; Kuan-Ta Chen; Hwai-Jung Hsu; Chin-Laung Lei (November 2012), "Forecasting online game addictiveness", Proc. IEEE/ACM NetGames 2012, Venice, Italy, retrieved 2013-10-17
  11. Ravaja N, Turpeinen M, Saari T, Puttonen S, Keltikangas-Järvinen L (February 2008). "The psychophysiology of James Bond: phasic emotional responses to violent video game events". Emotion. 8 (1): 114–20. doi:10.1037/1528-3542.8.1.114. PMID 18266521.
  12. Mandryk, R.L.; Atkins, M. (2007). "A Fuzzy Physiological Approach for Continuously Modeling Emotion During Interaction with Play Environments". International Journal of Human-Computer Studies. 6 (4): 329–47. doi:10.1016/j.ijhcs.2006.11.011.
  13. Hazlett, R. L. (2006). "Measuring emotional valence during interactive experiences: boys at video game play". Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '06): 1023–6. doi:10.1145/1124772.1124925. S2CID 17119148.
  14. Nacke, L. E.; Lindley, C. (December 2009). "Affective Ludology, Flow and Immersion in a First- Person Shooter: Measurement of Player Experience". Loading. 3 (5). arXiv:1004.0248.
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