Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter January 9, 2014

Frequency study of facial electromyography signals with respect to emotion recognition

  • Jerritta Selvaraj EMAIL logo , Murugappan Murugappan , Khairunizam Wan and Sazali Yaacob


Emotional intelligence is one of the key research areas in human-computer interaction. This paper reports the development of an emotion recognition system using facial electromyogram (EMG) signals focusing the ambiguity on the frequency ranges used by different research works. The six emotional states (happiness, sadness, fear, surprise, disgust, and neutral) were elicited in 60 subjects using audio visual stimuli. Statistical features were extracted from the signals at high, medium, low, and very low frequency levels. They were then classified using four classifiers – naïve Bayes, regression tree, K-nearest neighbor, and fuzzy K-nearest neighbor, and the performance of the system at the different frequency levels were studied using three metrics, namely, % accuracy, sensitivity, and specificity. The post hoc tests in analysis of variance (ANOVA) indicate that the features contain significant emotional information at the very low-frequency range (<0.08 Hz). Similarly, the performance metrics of the classifiers also ensure better recognition rate at very low-frequency range. Though this range of frequency has not been used by researchers, the results of this work indicate that it should not be ignored. Further investigation of the very low frequency range to identify emotional information is still in progress.

Corresponding author: Jerritta Selvaraj, Intelligent Signal Processing Research Cluster, School of Mechatronics Engineering, Universiti Malaysia Perlis (UNIMAP), Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia, Phone: +06-014-3005077, E-mail:


[1] Agrafioti F, Hatzinakos D, Anderson AK. ECG pattern analysis for emotion detection. IEEE Trans Affect Comput 2012; 3: 102–115.10.1109/T-AFFC.2011.28Search in Google Scholar

[2] Bal E, Harden E, Lamb D, Van Hecke A, Denver J, Porges S. Emotion recognition in children with autism spectrum disorders: relations to eye gaze and autonomic state. J Autism Dev Disord 2010; 40: 358–370.10.1007/s10803-009-0884-3Search in Google Scholar

[3] Boxtel Av. Facial EMG as a Tool for inferring affective states. In: Proceedings of Measuring Behavior, Eindhoven, The Netherlands, 2010: 104–108.Search in Google Scholar

[4] Chuan-Yu C, Jeng-Shiun T, Chi-Jane W, Pau-choo C. Emotion recognition with consideration of facial expression and physiological signals. In: Computational Intelligence in Bioinformatics and Computational Biology, 2009 CIBCB ′09 IEEE Symposium on 2009: 278–283.10.1109/CIBCB.2009.4925739Search in Google Scholar

[5] Ciaccio EJ, Weiner S, Reisman SS, Dunn SM, Akay M. Pattern recognition and interpretation of electromyogram data from cat jaw muscle. Comput Biol Med 1994; 24: 19–30.10.1016/0010-4825(94)90034-5Search in Google Scholar

[6] Cong Z, Chetouani M. Hilbert-Huang transform based physiological signals analysis for emotion recognition. In: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009: 334–339.10.1109/ISSPIT.2009.5407547Search in Google Scholar

[7] Martínez R, López de Ipiña K, Irigoyen E, et al. Emotion elicitation oriented to the development of a human emotion management system for people with intellectual disabilities. In: Demazeau Y, Dignum F, Corchado J, et al., editors. Trends in practical applications of agents and multiagent systems. Berlin/Heidelberg: Springer 2010: 689–696.10.1007/978-3-642-12433-4_81Search in Google Scholar

[8] Dutta A, Khattar B, Banerjee A. Nonlinear analysis of electromyogram following gait training with myoelectrically triggered neuromuscular electrical stimulation in stroke survivors. EURASIP J Adv Signal Process 2012; 2012: 153.10.1186/1687-6180-2012-153Search in Google Scholar

[9] Ekman P, Friesen WV. Universals and cultural differences in the judgments of facial expressions of emotion. J Pers Soc Psychol 1987; 53: 712–714.10.1037/0022-3514.53.4.712Search in Google Scholar

[10] Fred A, Filipe J, Gamboa H, Kim J, André E. Four-channel biosignal analysis and feature extraction for automatic emotion recognition. In: Ana Fred, Joaquim Filipe, Hugo Gamboa, editors. Biomedical Engineering Systems and Technologies. Berlin Heidelberg: Springer 2009: 265–277.10.1007/978-3-540-92219-3_20Search in Google Scholar

[11] Gouizi K, Reguig FB, Maaoui C. Emotion recognition from physiological signals. J Med Eng Technol 2011; 35: 300–307.10.3109/03091902.2011.601784Search in Google Scholar PubMed

[12] Jonghwa K, Ande E. Emotion recognition based on physiological changes in music listening. IEEE Trans Pattern Anal Mach Intell 2008; 30: 2067–2083.10.1109/TPAMI.2008.26Search in Google Scholar PubMed

[13] Kim K, Bang S, Kim S. Emotion recognition system using short-term monitoring of physiological signals. Med Biol Eng Comput 2004; 42: 419–427.10.1007/BF02344719Search in Google Scholar PubMed

[14] Lang PJ, Bradley MM, Cuthbert BN. International Affective Picture System (IAPS): Instruction manual and affective ratings. Technical Report A-5 2001.Search in Google Scholar

[15] Lisetti CL, Nasoz F. Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP J Appl Signal Process 2004; 2004: 1672–1687.Search in Google Scholar

[16] Luca CJD, Gilmore LD, Kuznetsov M, Roy SH. Filtering the surface EMG signal: movement artifact and baseline noise contamination. J Biomech 2010; 43: 1573–1579.10.1016/j.jbiomech.2010.01.027Search in Google Scholar PubMed

[17] Lundberg U, Kadefors R, Melin B, et al. Psychophysiological stress and EMG activity of the trapezius muscle. Int J Behav Med 1994; 1: 354–370.10.1207/s15327558ijbm0104_5Search in Google Scholar PubMed

[18] Maaoui C, Pruski A. Emotion recognition through physiological signals for human-machine communication. In: Kordic V, editor. Cutting edge robotics. Rijeka, Croatia: INTECH OPEN 2010.10.5772/10312Search in Google Scholar

[19] Maaoui C, Pruski A, Abdat F. Emotion recognition for human-machine communication. In: International Conference on Intelligent Robots and Systems, 2008 IROS 2008 IEEE/RSJ2008. p. 1210–1215.10.1109/IROS.2008.4650870Search in Google Scholar

[20] Mitchell P, Krotish D, Shin Y-J, Hirth V. Cross time-frequency analysis of gastrocnemius electromyographic signals in hypertensive and nonhypertensive subjects. EURASIP J Adv Signal Process 2010; 2010: 206560.10.1155/2010/206560Search in Google Scholar

[21] Panoulas KI, Hadjileontiadis LJ, Panas SM. Enhancement of R-wave detection in ECG data analysis using higher-order statistics. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001. p. 344–347, vol. 341.Search in Google Scholar

[22] Picard RW, Vyzas E, Healey J. Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 2001; 23: 1175–1191.10.1109/34.954607Search in Google Scholar

[23] Rattanyu K, Mizukawa M, Jacko J. Emotion recognition using biological signal in intelligent space. Hum Comput Interact 2011; 6763: 586–592.10.1007/978-3-642-21616-9_66Search in Google Scholar

[24] Ren P, Barreto A, Adjouadi M. Multi-step EMG classification algorithm for human-computer interaction. In: Sobh T, Elleithy K, editors. Innovations in computing sciences and software engineering. Netherlands: Springer 2010: 183–188.10.1007/978-90-481-9112-3_31Search in Google Scholar

[25] Scholesberg H. Three dimensions of emotion. Psychol Rev 1954; 61: 81–88.10.1037/h0054570Search in Google Scholar PubMed

[26] Tamietto M, de Gelder B. Emotional contagion for unseen bodily expressions: evidence from facial EMG. In: 8th IEEE International Conference on Automatic Face and Gesture Recognition, 2008 FG ′08 2008: 1–5.Search in Google Scholar

[27] Thusneyapan S, Zahalak GI. A practical electrode-array myoprocessor for surface electromyography. IEEE Trans Biomed Eng 1989; 36: 295–299.10.1109/10.16479Search in Google Scholar PubMed

[28] Westerink JDM, Broek EL, Schut MH, Herk J, Tuinenbreijer K. Computing emotion awareness through galvanic skin response and facial electromyography. In: Westerink JDM, Ouwerkerk M, Overbeek TM, Pasveer WF, Ruyter B, editors. Probing experience. The Netherlands: Springer 2008: 149–162.10.1007/978-1-4020-6593-4_14Search in Google Scholar

Received: 2013-6-24
Accepted: 2013-12-3
Published Online: 2014-1-9
Published in Print: 2014-6-1

©2014 by Walter de Gruyter Berlin/Boston

Downloaded on 8.2.2023 from
Scroll Up Arrow