:: Volume 8, Issue 3 (Summer - 2020) ::
Shefaye Khatam 2020, 8(3): 49-60 Back to browse issues page
Prediction of Dynamic Facial Emotional Expressions Valences Based on Absolute Brainwaves Power in Adolescents: Using Quantitative Electroencephalogram
Maryam Moshirian Farahi , Mohammad Javad Asghari Ebrahimabad * , Imanollah Bigdeli , Ali Gorji
Department of Psychology, Ferdowsi University of Mashhad, Mashhad, Iran , mjasghari@um.ac.ir
Abstract:   (2500 Views)
Introduction: Adolescent brain development is recognized by changes in the brain structure and functions. Emotional processing could be affected by these brain changes. The aim of present study was to predict the dynamic emotional processing valences based on absolute brainwaves power (delta, theta, beta, and alpha bands) of five cortical regions. Materials and Methods: The study population was 50 healthy adolescents living in Mashhad, Iran. The Tools included mental state interview, EEG device, and Dynamic Emotional Processing Valences Task. Results: To predict the valences of facial expressions, one model was extracted for sadness and disgust based on the stepwise regression. The beta band in frontal area (for fear), theta band in frontal and beta in central area (for surprise), and beta band in frontal as well as theta band in frontal cortical regions were extracted. Conclusion: The hypotheses of predictably of dynamic facial expressions is supported by cortical electrophysiological activities during adolescence, and these cortical activities have a number of differences and similarities in comparison to adulthood. Finally, it is recommended that methods, such as Neurofeedback, could be applied to modulate adolescence emotional problems.
Keywords: Facial Expression, Electroencephalography, Brain Waves, Emotions
Full-Text [PDF 1427 kb]   (1086 Downloads)    
Type of Study: Research --- Open Access, CC-BY-NC | Subject: Basic research in Neuroscience
References
1. Williams MA, McGlone F, Abbott DF, Mattingley JB. Differential amygdala responses to happy and fearful facial expressions depend on selective attention. Neuroimage. 2005; 24(2): 417-25. [DOI:10.1016/j.neuroimage.2004.08.017]
2. Haxby JV, Hoffman EA, Gobbini MI. Human neural systems for face recognition and social communication. Biol Psychiatry. 2002; 51(1): 59-67. [DOI:10.1016/S0006-3223(01)01330-0]
3. Bigdeli I, Rezaee M, Azami S, Hemati F. The comparison of emotional recognition between outpatients with borderline personality disorder and normal people. Iran J Cog and Edu. 2014; 1(1): 7-12.
4. Chen J, Liu X, Tu P, Aragones A. Learning person-specific models for facial expression and action unit recognition. Pattern Recognit Lett. 2013; 34(15): 1964-70. [DOI:10.1016/j.patrec.2013.02.002]
5. Hinojosa JA, Mercado F, Carretié L. N170 sensitivity to facial expression: A meta-analysis. Neurosci Biobehav Rev. 2015; 55: 498-509. [DOI:10.1016/j.neubiorev.2015.06.002]
6. Gold JM, Barker JD, Barr S, Bittner JL, Bromfield WD, Chu N, et al. The efficiency of dynamic and static facial expression recognition. J Vis. 2013; 13(5): 23-23. [DOI:10.1167/13.5.23]
7. Hehman E, Flake JK, Freeman JB. Static and dynamic facial cues differentially affect the consistency of social evaluations. Pers Soc Psychol Bull. 2015; 41(8): 1123-34. [DOI:10.1177/0146167215591495]
8. Lynch TR, Rosenthal MZ, Kosson DS, Cheavens JS, Lejuez CW, Blair RJ. Heightened sensitivity to facial expressions of emotion in borderline personality disorder. Emotion. 2006; 6(4): 647-55. [DOI:10.1037/1528-3542.6.4.647]
9. De Wied M, van Boxtel A, Zaalberg R, Goudena PP, Matthys W. Facial EMG responses to dynamic emotional facial expressions in boys with disruptive behavior disorders. J Psychiatr Res. 2006; 40(2): 112-121. [DOI:10.1016/j.jpsychires.2005.08.003]
10. Bal E, Harden E, Lamb D, Van Hecke AV, Denver JW, Porges SW. Emotion recognition in children with autism spectrum disorders: Relations to eye gaze and autonomic state. J Autism Dev Disord. 2010; 40(3): 358-70. [DOI:10.1007/s10803-009-0884-3]
11. Zwick JC, Wolkenstein L. Facial emotion recognition, theory of mind and the role of facial mimicry in depression. J Affect Disord. 2017; 210: 90-9. [DOI:10.1016/j.jad.2016.12.022]
12. Gutiérrez-García A, Calvo MG. Social anxiety and threat-related interpretation of dynamic facial expressions: Sensitivity and response bias. Pers Individ Dif. 2017; 107: 10-6. [DOI:10.1016/j.paid.2016.11.025]
13. DeYoung CG, Hirsh JB, Shane MS, Papademetris X, Rajeevan, N, Gray JR. Testing predictions from personality neuroscience: brain structure and the big five. Psychol Sci. 2010; 21(6): 820-8. [DOI:10.1177/0956797610370159]
14. Davidson RJ. Anterior cerebral asymmetry and the nature of emotion. Brain Cogn. 1992; 20(1): 125-51. [DOI:10.1016/0278-2626(92)90065-T]
15. Harmon-Jones E, Sigelman J. State anger and prefrontal brain activity: evidence that insult-related relative left-prefrontal activation is associated with experienced anger and aggression. J Pers Soc Psychol. 2001; 80: 797-803. [DOI:10.1037/0022-3514.80.5.797]
16. Murugappan MN, Nagarajan R, Yaacob S. Comparison of different wavelet features from EEG signals for classifying human emotions. In2009 IEEE Symposium on Industrial Electronics & Applications. 2009; 2: 836-41. [DOI:10.1109/ISIEA.2009.5356339]
17. Yuen CT, San San W, Seong TC, Rizon M. Classification of human emotions from EEG signals using statistical features and neural network. International Journal of Integrated Engineering. 2009; 1(3).
18. Liu Y, Sourina O, Nguyen MK. Real-time EEG-based human emotion recognition and visualization. In 2010 International Conference on Cyberworlds. 2010; 262-9. [DOI:10.1109/CW.2010.37]
19. Horlings R, Datcu D, Rothkrantz LJ. Emotion recognition using brain activity. In Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing. 2008; 6. [DOI:10.1145/1500879.1500888]
20. Knyazev GG. Antero-posterior EEG spectral power gradient as a correlate of extraversion and behavioral inhibition. Open Neuroimag J. 2010; 4: 114-20. [DOI:10.2174/1874440001004010114]
21. Aftanas LI, Varlamov AA, Pavlov SV, Makhnev VP, Reva NV. Affective picture processing: Event-related synchronization within individually defined human theta band is modulated by valence dimension. Neurosci Lett. 2001; 303(2): 115-8. [DOI:10.1016/S0304-3940(01)01703-7]
22. Balconi M, Lucchiari C. EEG correlates (event-related desynchronization) of emotional face elaboration: A temporal analysis. Neurosci Lett. 2006; 392(1-2): 118-23. [DOI:10.1016/j.neulet.2005.09.004]
23. Othman M, Wahab A, Karim I, Dzulkifli MA, Alshaikli IFT. EEG emotion recognition based on the dimensional models of emotions. Procedia Soc Behav Sci. 2013; 6(97): 30-7. [DOI:10.1016/j.sbspro.2013.10.201]
24. Li M, Lu BL. Emotion classification based on gamma-band EEG. In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2009; 1223-6.
25. Sato W, Kochiyama T, Uono S. Spatiotemporal neural network dynamics for the processing of dynamic facial expressions. Sci Rep. 2015; 5: 12432. [DOI:10.1038/srep12432]
26. Wang S, Zhao Y, Chen S, Lin G, Sun P, Wang, T. EEG biofeedback improves attentional bias in high trait anxiety individuals. BMC Neurosci. 2013; 14(1): 115. doi: 10.1186/1471-2202-14-115. [DOI:10.1186/1471-2202-14-115]
27. Petroni A, Canales-Johnson A, Urquina H, Guex R, Hurtado E, Blenkmann A, et al. The cortical processing of facial emotional expression is associated with social cognition skills and executive functioning: a preliminary study. Neurosci Lett. 2011; 505(1): 41-6. [DOI:10.1016/j.neulet.2011.09.062]
28. Van der Schalk J, Hawk ST, Fischer AH, Doosje B. Moving faces, looking places: validation of the amsterdam dynamic facial expression set (ADFES). Emotion. 2011; 11(4): 907-20. [DOI:10.1037/a0023853]
29. Ertl M, Hildebrandt M, Ourina K, Leicht G, Mulert, C. Emotion regulation by cognitive reappraisal - The role of frontal theta oscillations. Neuroimage. 2013; 81: 412-21. [DOI:10.1016/j.neuroimage.2013.05.044]
30. Kropotov JD. Functional neuromarkers for psychiatry: applications for diagnosis and treatment. functional neuromarkers for psychiatry: Applications for diagnosis and treatment. Academic Press. 2016. [DOI:10.1016/B978-0-12-410513-3.00041-3]
31. Gordeev SA. Clinical-psychophysiological studies of patients with panic attacks with and without agoraphobic disorders. Neuroscience and Behavioral Physiology. 2008; 38(6): 633-7. [DOI:10.1007/s11055-008-9016-3]
32. Pavlenko VB, Chernyi SV, Goubkina DG. Eeg correlates of anxiety and emotional stability in adult healthy subjects. Neurophysiology. 2009; 41(5): 337-45. [DOI:10.1007/s11062-010-9111-2]
33. De Carvalho MR, Velasques BB, Freire RC, Cagy M, Marques JB, Teixeira S, et al. Frontal cortex absolute beta power measurement in Panic Disorder with Agoraphobia patients. Journal of Affective Disorders. 2015; 184: 176-81. [DOI:10.1016/j.jad.2015.05.055]
34. Abhang PA, Gawali BW, Mehrotra SC. Introduction to EEG- and Speech-Based Emotion Recognition. Introduction to EEG- and Speech-Based Emotion Recognition. Academic Press. 2016. [DOI:10.1016/B978-0-12-804490-2.00005-1]
35. Hofman D, Schutter DJ. Asymmetrical frontal resting-state beta oscillations predict trait aggressive tendencies and behavioral inhibition. Soc Cogn Affect Neurosci. 2011; 7(7): 850-7. [DOI:10.1093/scan/nsr060]

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