The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences Volume 2: Performing Research
Book contents
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Preface
- Part I Quantitative Data Collection Sources
- Part II Important Methodological Considerations
- Part III Self-Report Measures
- Part IV Behavioral Measures
- Part V Physiological Measures
- 19 Measuring Hormones: Considerations for Biospecimen Collection, Assay, and Analysis
- 20 Cardiovascular Measures for Social and Behavioral Research
- 21 Electrodermal Activity: Applications and Challenges
- 22 Surface Electromyography
- 23 EEG and ERP
- Part VI Qualitative Data Collection Sources
- Index
- References
22 - Surface Electromyography
from Part V - Physiological Measures
Published online by Cambridge University Press: 12 December 2024
Book contents
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Cambridge Handbooks in Psychology
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral Sciences
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Contributors
- Preface
- Part I Quantitative Data Collection Sources
- Part II Important Methodological Considerations
- Part III Self-Report Measures
- Part IV Behavioral Measures
- Part V Physiological Measures
- 19 Measuring Hormones: Considerations for Biospecimen Collection, Assay, and Analysis
- 20 Cardiovascular Measures for Social and Behavioral Research
- 21 Electrodermal Activity: Applications and Challenges
- 22 Surface Electromyography
- 23 EEG and ERP
- Part VI Qualitative Data Collection Sources
- Index
- References
Summary
Surface electromyography (EMG) measurements provide a non-invasive way to measure physical behavior in a way that is more sensitive and less prone to bias compared to observational methods. This chapter covers the use of EMG in social and behavioral research. First, the biological underpinnings of muscle activity are briefly reviewed to give the reader a basic understanding of the signal being measured. Next, the steps for obtaining the EMG signal are covered, including equipment and signal processing. Finally, some common use cases of EMG measures in social and behavioral research are reviewed. With modern-day equipment, EMG measures can be collected both in the traditional laboratory setting and, when signal noise concerns are acknowledged, in the “real world.”
- Type
- Chapter
- Information
- The Cambridge Handbook of Research Methods and Statistics for the Social and Behavioral SciencesVolume 2: Performing Research, pp. 497 - 518Publisher: Cambridge University PressPrint publication year: 2024
References
AbuHasan, Q., & Munakomi, S. (2022). Neuroanatomy, Pyramidal Tract. StatPearls Publishing. Available from www.ncbi.nlm.nih.gov/books/NBK545314Google Scholar
Agnihotri, H., Paul, M., & Sandhu, J. S. (2008). The comparative efficacy of two biofeedback techniques in the treatment of generalized anxiety disorder. Pakistan Journal of Social and Clinical Psychology, 6(1), 35–46.Google Scholar
Andreassi, J. L. (2013). Psychophysiology: Human behavior & physiological response, 4th ed. Psychology Press.CrossRefGoogle Scholar
Arns, M., Conners, C. K., & Kraemer, H. C. (2013). A decade of EEG theta/beta ratio research in ADHD: A meta-analysis. Journal of Attention Disorders, 17(5), 374-383.CrossRefGoogle ScholarPubMed
Bakhshayesh, A. R., Hänsch, S., Wyschkon, A., Rezai, M. J., & Esser, G. (2011). Neurofeedback in ADHD: A single-blind randomized controlled trial. European Child & Adolescent Psychiatry, 20, 481–491.CrossRefGoogle ScholarPubMed
Barth, B., Mayer, K., Strehl, U., Fallgatter, A. J., & Ehlis, A. C. (2017). EMG biofeedback training in adult attention-deficit/hyperactivity disorder: An active (control) training? Behavioural Brain Research, 329, 58-66.CrossRefGoogle ScholarPubMed
Baschnagel, J. S., Coffey, S. F., Hawk, L. W. Jr., Schumacher, J. A., & Holloman, G. (2013). Psychophysiological assessment of emotional processing in patients with borderline personality disorder with and without comorbid substance use. Personality Disorders: Theory, Research, and Treatment, 4(3), 203–213.CrossRefGoogle ScholarPubMed
Baschnagel, J. S., & Edlund, J. E. (2016). Affective modification of the startle eyeblink response during sexual and emotional infidelity scripts. Evolutionary Psychological Science, 2, 114–122.CrossRefGoogle Scholar
Bazanova, O. M., Auer, T., & Sapina, E. A. (2018). On the efficiency of individualized theta/beta ratio neurofeedback combined with forehead EMG training in ADHD children. Frontiers in Human Neuroscience, 12, 3. https://doi.org/10.3389/fnhum.2018.00003CrossRefGoogle ScholarPubMed
Blumenthal, T. D., Cuthbert, B. N., Filion, D. L., Hackley, S., Lipp, O. V., & van Boxtel, A. (2005). Committee report: Guidelines for human startle eyeblink electromyographic studies. Psychophysiology, 42(1), 1-15. https://doi.org/10.1111/j.1469-8986.2005.00271.xCrossRefGoogle ScholarPubMed
Blumenthal, T. D., Elden, A., & Flaten, M. A. (2004). A comparison of several methods used to quantify prepulse inhibition of eyeblink responding. Psychophysiology, 41(2), 326–332.CrossRefGoogle ScholarPubMed
Bowditch, H. P., & Warren, J. W. (1890). The knee-jerk and its physiological modifications. Journal of Physiology, 11(1–2), 25–64. https://doi.org/10.1113/jphysiol.1890.sp000318CrossRefGoogle ScholarPubMed
Bowles, C., Smith, J., & Parker, K. (1979). EMG biofeedback and progressive relaxation training: A comparative study of two groups of normal subjects. Western Journal of Nursing Research, 1(3), 179–189. https://doi.org/10.1177/019394597900100304CrossRefGoogle ScholarPubMed
Bradley, M. M., Codispoti, M., Cuthbert, B. N., & Lang, P. J. (2001). Emotion and motivation I: Defensive and appetitive reactions in picture processing. Emotion, 1(3), 276–298.CrossRefGoogle ScholarPubMed
Bradley, M. M., Cuthbert, B. N., & Lang, P. J. (1990). Startle reflex modification: Emotion or attention? Psychophysiology, 27(5), 513–522.CrossRefGoogle ScholarPubMed
Braff, D. L., & Geyer, M. A. (1990). Sensorimotor gating and schizophrenia: Human and animal model studies. Archives of General Psychiatry, 47, 181–188.CrossRefGoogle ScholarPubMed
Budzynski, T. H., Stoyva, J. M., Adler, C. S., & Mullaney, D. J. (1973). EMG biofeedback and tension headache: A controlled outcome study. Psychosomatic Medicine, 35(6), 484-496. https://doi.org/10.1097/00006842-197311000-00004CrossRefGoogle ScholarPubMed
Choromański, W., Grabarek, I., & Kozłowski, M. (2021). Integrated design of a custom steering system in cars and verification of its correct functioning. Energies, 14(20), 1–9.CrossRefGoogle Scholar
Chowdhury, D. (2012). Tension type headache. Annals of Indian Academy of Neurology, 15(Suppl. 1), S83–S88. https://doi.org/10.4103/0972-2327.100023CrossRefGoogle ScholarPubMed
Cobb, D. E., & Evans, J. R. (1981). The use of biofeedback techniques with school-aged children exhibiting behavioral and/or learning problems. Journal of Abnormal Child Psychology, 9, 251–281.CrossRefGoogle ScholarPubMed
Dichter, G. S., Benning, S. D., Holtzclaw, T. N., & Bodfish, J. W. (2010). Affective modulation of the startle eyeblink and postauricular reflexes in autism spectrum disorder. Journal of Autism and Developmental Disorders, 40, 858–869.CrossRefGoogle ScholarPubMed
Dimberg, U. (1988). Facial electromyography and the experience of emotion. Journal of Psychophysiology, 2(4), 277–282.Google Scholar
Dimberg, U. (1990). Facial electromyography and emotional reactions. Psychophysiology, 27(5), 481–494. https://doi.org/10.1111/j.1469-8986.1990.tb01962.xCrossRefGoogle ScholarPubMed
Fridlund, A. J., & Cacioppo, J. T. (1986). Guidelines for human electromyographic research. Psychophysiology, 23(5), 567–589.CrossRefGoogle ScholarPubMed
Garcia, M. C., & Vieira, T. M. M. (2011). Surface electromyography: Why, when and how to use it. Revista andaluza de medicina del deporte, 4(1), 17–28.Google Scholar
Giggins, O. M., Persson, U. M., & Caulfield, B. (2013). Biofeedback in rehabilitation. Journal of Neuroengineering and Rehabilitation, 10, 60. https://doi.org/10.1186/1743-0003-10-60CrossRefGoogle ScholarPubMed
Gomez-Gil, J., San-Jose-Gonzalez, I., Nicolas-Alonso, L. F., & Alonso-Garcia, S. (2011). Steering a tractor by means of an EMG-based human–machine interface. Sensors, 11(7), 7110–7126.CrossRefGoogle ScholarPubMed
Grillon, C., Morgan III, C. A., Davis, M., & Southwick, S. M. (1998). Effects of experimental context and explicit threat cues on acoustic startle in Vietnam veterans with posttraumatic stress disorder. Biological Psychiatry, 44(10), 1027–1036.CrossRefGoogle ScholarPubMed
Hawk, L. W., & Kowmas, A. D. (2003). Affective modulation and prepulse inhibition of startle among undergraduates high and low in behavioral inhibition and approach. Psychophysiology, 40(1), 131–138.CrossRefGoogle ScholarPubMed
Herbert, C., & Kissler, J. (2010). Motivational priming and processing interrupt: Startle reflex modulation during shallow and deep processing of emotional words. International Journal of Psychophysiology, 76(2), 64–71.CrossRefGoogle ScholarPubMed
Hermens, H. J., Freriks, B., Disselhorst-Klug, C., & Rau, G. (2000). Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyography and Kinesiology, 10(5), 361–374.CrossRefGoogle ScholarPubMed
Jali, M. H., Bohari, Z. H., Sulaima, M. F., Nasir, M. N. M., & Jaafar, H. I. (2014). Classification of EMG signal based on human percentile using SOM. Research Journal of Applied Sciences, Engineering and Technology, 8(2), 235–242.CrossRefGoogle Scholar
Karlik, B. (2014). Machine learning algorithms for characterization of EMG signals. International Journal of Information and Electronics Engineering, 4(3), 189–194.CrossRefGoogle Scholar
Keltner, D., & Ekman, P. (2000). Facial expression of emotion. In Lewis, M., & Haviland-Jones, J. (eds.), Handbook of Emotions, 2nd ed. (pp. 236–249). Guilford Press.Google Scholar
Kundu, A. S., Mazumder, O., Lenka, P. K., & Bhaumik, S. (2018). Hand gesture recognition based omnidirectional wheelchair control using IMU and EMG sensors. Journal of Intelligent & Robotic Systems, 91, 529–541.CrossRefGoogle Scholar
Lang, A., & Yegiyan, N.S. (2014). Mediated substance cues: Motivational reactivity and use influence responses to pictures of alcohol. Journal of Health Communication, 19, 1216–1231.CrossRefGoogle ScholarPubMed
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1990). Emotion, attention, and the startle reflex. Psychological Review, 97(3), 377–395.CrossRefGoogle ScholarPubMed
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International Affective Picture System (IAPS): Instruction Manual and Affective Ratings. Technical Report A-8. Center for Research in Psychophysiology, University of Florida.Google Scholar
Larsen, J. T., Norris, C. J., & Cacioppo, J. T. (2003). Effects of positive and negative affect on electromyographic activity over zygomaticus major and corrugator supercilii. Psychophysiology, 40(5), 776–785. https://doi.org/10.1111/1469-8986.00078CrossRefGoogle ScholarPubMed
Lee, S. W. (1991). Biofeedback as a treatment for childhood hyperactivity: A critical review of the literature. Psychological Reports, 68(1), 163–192.CrossRefGoogle ScholarPubMed
Li, S., Walters, G., Packer, J., & Scott, N. (2018). Using skin conductance and facial electromyography to measure emotional responses to tourism advertising. Current Issues in Tourism, 21, 61–1783. https://doi.org/10.1080/13683500.2016.1223023CrossRefGoogle Scholar
Matzke, B., Herpertz, S. C., Berger, C., Fleischer, M., & Domes, G. (2014). Facial reactions during emotion recognition in borderline personality disorder: A facial electromyography study. Psychopathology, 47(2), 101–110. https://doi.org/10.1159/000351122.CrossRefGoogle ScholarPubMed
Maurizio, S., Liechti, M. D., Heinrich, H., Jäncke, L., Steinhausen, H.-C., Walitza, S., et al. (2014). Comparing tomographic EEG neurofeedback and EMG biofeedback in children with attention-deficit/hyperactivity disorder. Biological Psychology, 95, 31–44. https://doi.org/10.1016/j.biopsycho.2013.10.008CrossRefGoogle ScholarPubMed
McCoggle, M., Wilson, S., Rivera, A., Alba-Flores, R., & Soloiu, V. (2022). Biosensors based controller for small unmanned aerial vehicle navigation. In SoutheastCon 2022. IEEE. https://doi.org/10.1109/southeastcon48659.2022.9764015Google Scholar
Miller, M. W., Patrick, C. J., & Levenston, G. K. (2002). Affective imagery and the startle response: Probing mechanisms of modulation during pleasant scenes, personal experiences, and discrete negative emotions. Psychophysiology, 39(4), 519–529. https://doi.org/10.1017/S0048577202394095CrossRefGoogle ScholarPubMed
Monteiro, P., Tavares, D. L., Mourão, L., Nouws, H. P., & Maia, G. (2022). Biosensors, biofeedback, and neurofeedback. In Marques, A. & Queirós, R. (eds.), Digital Therapies in Psychosocial Rehabilitation and Mental Health (pp. 303–320). IGI Global. https://doi.org/10.4018/978-1-7998-8634-1.ch015CrossRefGoogle Scholar
Morrison, M. A., Trinder, K. M., & Morrison, T. G. (2019). Affective responses to gay men using facial electromyography: Is there a psychophysiological “look” of anti-gay bias. Journal of Homosexuality, 66(9), 1238–1261. https://doi.org/10.1080/00918369.2018.1500779CrossRefGoogle Scholar
Nacpil, E. J. C., Zheng, R., Kaizuka, T., & Nakano, K. (2019). A surface electromyography controlled steering assistance interface. Journal of Intelligent and Connected Vehicles, 2(1), 1–13.CrossRefGoogle Scholar
Paulhus, D. L. (1984). Two-component models of socially desirable responding. Journal of Personality and Social Psychology, 46(3), 598–609.CrossRefGoogle Scholar
Paulus, A., Renn, K., & Wentura, D. (2019). One plus one is more than two: The interactive influence of group membership and emotional facial expressions on the modulation of the affective startle reflex. Biological Psychology, 142, 140–146.CrossRefGoogle ScholarPubMed
Pole, N., Neylan, T. C., Otte, C., Henn-Hasse, C., Metzler, T. J., & Marmar, C. R. (2009). Prospective prediction of posttraumatic stress disorder symptoms using fear potentiated auditory startle responses. Biological psychiatry, 65(3), 235–240.CrossRefGoogle ScholarPubMed
Reicherts, P., Gerdes, A. B., Pauli, P., & Wieser, M. J. (2013). On the mutual effects of pain and emotion: Facial pain expressions enhance pain perception and vice versa are perceived as more arousing when feeling pain. Pain, 154(6), 793–800. https://doi.org/10.1016/j.pain.2013.02.012CrossRefGoogle Scholar
Rokicki, L. A., Houle, T. T., Dhingra, L. K., Weinland, S. R., Urban, A. M., & Bhalla, R. K. (2003). A preliminary analysis of EMG variance as an index of change in EMG biofeedback treatment of tension-type headache. Applied Psychophysiology and Biofeedback, 28(3), 205–215. https://doi.org/10.1023/a:1024633230584CrossRefGoogle Scholar
Rozga, A., King, T. Z., Vuduc, R. W., & Robins, D. L. (2013). Undifferentiated facial electromyography responses to dynamic, audio-visual emotion displays in individuals with autism spectrum disorders. Developmental Science, 16(4), 499–514. https://doi.org/10.1111/desc.12062CrossRefGoogle ScholarPubMed
Sabbah, P., De Schonen, S., Leveque, C., Gay, S., Pfefer, F., Nioche, C., et al. (2002). Sensorimotor cortical activity in patients with complete spinal cord injury: A functional magnetic resonance imaging study. Journal of Neurotrauma, 19(1), 53–60.CrossRefGoogle ScholarPubMed
Schoenberg, P. L., & David, A. S. (2014). Biofeedback for psychiatric disorders: A systematic review. Applied Psychophysiology and Biofeedback, 39(2), 109–135. https://doi.org/10.1007/s10484-014-9246-9CrossRefGoogle ScholarPubMed
Shair, E. F., Razali, R. H., Abdullah, A. R., & Jamaluddin, N. F. (2022). EMG pattern recognition using TFD for future control of in-car electronic equipment. International Journal of Fuzzy Logic and Intelligent Systems, 22(1), 11–22.CrossRefGoogle Scholar
Stern, R. M., Ray, W. J., & Quigley, K. S. (2001). Psychophysiological Recording. Oxford University Press.Google Scholar
Tassinary, L. G., Cacioppo, J. T., & Vanman, E. J. (2007). The skeletomotor system: Surface electromyography. In Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (eds.), Handbook of Psychophysiology (pp. 267–299). Cambridge University Press.CrossRefGoogle Scholar
Tunis, M. M., & Wolff, H. G. (1954). Studies on headache: Cranial artery vasoconstriction and muscle contraction headache. AMA Archives of Neurology & Psychiatry, 71(4), 425–434.CrossRefGoogle ScholarPubMed
Vanman, E. J., Saltz, J. L., Nathan, L. R., & Warren, J. A. (2004). Racial discrimination by low-prejudiced whites: Facial movements as implicit measures of attitudes related to behavior. Psychological Science, 15(11), 711–714.CrossRefGoogle ScholarPubMed
Varcin, K. J., Nangle, M. R., Henry, J. D., Bailey, P. E., & Richmond, J. L. (2019). Intact spontaneous emotional expressivity to non-facial but not facial stimuli in schizophrenia: An electromyographic study. Schizophrenia Research, 206, 37–42. https://doi.org/10.1016/j.schres.2018.12.019CrossRefGoogle Scholar
Vigliotta, J., Cipleu, J., Mikell, A., & Alba-Flores, R. (2021). EMG controlled electric wheelchair. Lecture Notes in Networks and Systems, 296, 439–449. https://doi.org/10.1007/978-3-030-82199-9_29CrossRefGoogle Scholar
Vrana, S. R., Spence, E. L., & Lang, P. J. (1988). The startle probe response: A new measure of emotion? Journal of Abnormal Psychology, 97(4), 487–491. https://doi.org/10.1037/0021-843X.97.4.487CrossRefGoogle ScholarPubMed
Watson, T. (n.d.). EMG Biofeedback – The Principles. Herman & Wallace Pelvic Rehabilitation Institute. https://hermanwallace.com/images/Tim_Watson_Biofeedback_Intro.pdf (retrieved February 20, 2023).Google Scholar
Wolf, S. L. (1978). Essential considerations in the use of EMG biofeedback. Physical Therapy, 58(1), 25–31. https://doi.org/10.1093/ptj/58.1.25CrossRefGoogle ScholarPubMed
Yartz, A. R., & Hawk, L. W. Jr. (2002). Addressing the specificity of affective startle modulation: Fear versus disgust. Biological Psychology, 59(1), 55–68.CrossRefGoogle ScholarPubMed
Yerkes, R. M. (1905). The sense of hearing in frogs. Journal of Comparative Neurology and Psychology, 15, 279–304. https://doi.org/10.1002/cne.920150402CrossRefGoogle Scholar
Yousefi, J., & Hamilton-Wright, A. (2014). Characterizing EMG data using machine-learning tools. Computers in Biology and Medicine, 51, 1–13.CrossRefGoogle ScholarPubMed