Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Biomedical Engineering / Biomedizinische Technik

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering and the German Society of Biomaterials

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Habibović, Pamela / Haueisen, Jens / Jahnen-Dechent, Wilhelm / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Lenarz, Thomas / Leonhardt, Steffen / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Boenick, Ulrich / Jaramaz, Branislav / Kraft, Marc / Lenthe, Harry / Lo, Benny / Mainardi, Luca / Micera, Silvestro / Penzel, Thomas / Robitzki, Andrea A. / Schaeffter, Tobias / Snedeker, Jess G. / Sörnmo, Leif / Sugano, Nobuhiko / Werner, Jürgen /


IMPACT FACTOR 2017: 1.096
5-year IMPACT FACTOR: 1.492

CiteScore 2017: 0.48

SCImago Journal Rank (SJR) 2017: 0.202
Source Normalized Impact per Paper (SNIP) 2017: 0.356

Online
ISSN
1862-278X
See all formats and pricing
More options …
Volume 59, Issue 3

Issues

Volume 57 (2012)

Frequency study of facial electromyography signals with respect to emotion recognition

Jerritta Selvaraj
  • Corresponding author
  • Intelligent Signal Processing Research Cluster, School of Mechatronics Engineering, Universiti Malaysia Perlis (UNIMAP), Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Murugappan Murugappan
  • Intelligent Signal Processing Research Cluster, School of Mechatronics Engineering, Universiti Malaysia Perlis (UNIMAP), Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Khairunizam Wan
  • Intelligent Signal Processing Research Cluster, School of Mechatronics Engineering, Universiti Malaysia Perlis (UNIMAP), Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Sazali Yaacob
  • Intelligent Signal Processing Research Cluster, School of Mechatronics Engineering, Universiti Malaysia Perlis (UNIMAP), Kampus Pauh Putra, 02600, Arau, Perlis, Malaysia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2014-01-09 | DOI: https://doi.org/10.1515/bmt-2013-0118

Abstract

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.

Keywords: analysis of variance; audio visual stimuli; emotional frequency analysis; human-computer interaction; facial electromyogram signals; sensitivity; specificity

References

  • [1]

    Agrafioti F, Hatzinakos D, Anderson AK. ECG pattern analysis for emotion detection. IEEE Trans Affect Comput 2012; 3: 102–115.CrossrefGoogle 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.PubMedCrossrefWeb of ScienceGoogle Scholar

  • [3]

    Boxtel Av. Facial EMG as a Tool for inferring affective states. In: Proceedings of Measuring Behavior, Eindhoven, The Netherlands, 2010: 104–108.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.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.PubMedCrossrefGoogle 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.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.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.Web of ScienceGoogle 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.PubMedCrossrefGoogle 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.Google Scholar

  • [11]

    Gouizi K, Reguig FB, Maaoui C. Emotion recognition from physiological signals. J Med Eng Technol 2011; 35: 300–307.PubMedCrossrefGoogle Scholar

  • [12]

    Jonghwa K, Ande E. Emotion recognition based on physiological changes in music listening. IEEE Trans Pattern Anal Mach Intell 2008; 30: 2067–2083.CrossrefWeb of ScienceGoogle Scholar

  • [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.PubMedGoogle Scholar

  • [14]

    Lang PJ, Bradley MM, Cuthbert BN. International Affective Picture System (IAPS): Instruction manual and affective ratings. Technical Report A-5 2001.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.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.PubMedWeb of ScienceCrossrefGoogle Scholar

  • [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.PubMedCrossrefGoogle Scholar

  • [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.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.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.Web of ScienceGoogle 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.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.CrossrefGoogle Scholar

  • [23]

    Rattanyu K, Mizukawa M, Jacko J. Emotion recognition using biological signal in intelligent space. Hum Comput Interact 2011; 6763: 586–592.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.Google Scholar

  • [25]

    Scholesberg H. Three dimensions of emotion. Psychol Rev 1954; 61: 81–88.CrossrefGoogle Scholar

  • [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.Google Scholar

  • [27]

    Thusneyapan S, Zahalak GI. A practical electrode-array myoprocessor for surface electromyography. IEEE Trans Biomed Eng 1989; 36: 295–299.CrossrefPubMedGoogle Scholar

  • [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.Google Scholar

About the article

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:


Received: 2013-06-24

Accepted: 2013-12-03

Published Online: 2014-01-09

Published in Print: 2014-06-01


Citation Information: Biomedical Engineering / Biomedizinische Technik, Volume 59, Issue 3, Pages 241–249, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: https://doi.org/10.1515/bmt-2013-0118.

Export Citation

©2014 by Walter de Gruyter Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in