Pengukuran Aktivitas Elektrodermal  untuk Penelitian Psikologi

Zulfikri Khakim

doi:10.22146/buletinpsikologi.87294

Pengukuran Aktivitas Elektrodermal untuk Penelitian Psikologi

Zulfikri Khakim

(Submitted 25 July 2023)

(Published 18 December 2023)

Abstract

Aktivitas Elektrodermal (EDA), atau yang kerap pula disebut sebagai Galvanic Skin Response (GSR) merujuk pada pengukuran aktivitas elektris pada permukaan kulit. Sensor EDA mengukur properti kelistrikan pada kulit sebagai indikator banyaknya keringat pada kulit. Kadar keringat tersebut dapat mencerminkan aktivitas saraf simpatetik yang muncul dalam berbagai kondisi seperti stres, gugahan emosi, hingga beban kognitif. Karena sifatnya yang portabel, noninvasif, dan memberikan pengukuran yang objektif, EDA telah banyak digunakan dalam penelitian psikologi untuk menyelidiki proses-proses mental terkait dalam berbagai konteks. Artikel ini memberikan pengenalan mengenai pengukuran EDA yang terdiri atas dasar asumsi pengukuran, teknis pengukuran, kuantifikasi dan analisis data hingga contoh parameter konstruk psikologis yang dapat diteliti dengan metode EDA.

Keywords

Electrodermal Activity; Galvanic Skin Response; Psychology; Skin Conductance Response

Full Text: PDF

DOI: 10.22146/buletinpsikologi.87294

References

Baker, L. B. (2019). Physiology of sweat gland function: The roles of sweating and sweat composition in human health. Temperature: Multidisciplinary Biomedical Journal, 6(3), 211. https://doi.org/10.1080/23328940.2019.1632145

Bakker, J., Pechenizkiy, M., & Sidorova, N. (2011). What’s your current stress level? Detection of stress patterns from GSR sensor data. Proceedings - IEEE International Conference on Data Mining, ICDM, 1, 573–580. https://doi.org/10.1109/ICDMW.2011.178

Benedek, M., & Kaernbach, C. (2010a). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80–91. https://doi.org/10.1016/J.JNEUMETH.2010.04.028

Benedek, M., & Kaernbach, C. (2010b). Decomposition of skin conductance data by means of nonnegative deconvolution. Psychophysiology, 47(4), 647–658. https://doi.org/10.1111/J.1469-8986.2009.00972.X

Boucsein, W. (2012). Electrodermal Activity.

Boucsein, W., Fowles, D. C., Grimnes, S., Ben-Shakhar, G., Roth, W. T., Dawson, M. E., & Filion, D. L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49(8), 1017–1034. https://doi.org/10.1111/j.1469-8986.2012.01384.x

Brouwer, A.-M., Zander, T. O., van Erp, J. B. F., Korteling, J. E., & Bronkhorst, A. W. (2015). Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls. Frontiers in Neuroscience, 9. https://doi.org/10.3389/fnins.2015.00136

Cantarella, S., Hillenbrand, C., Aldridge-Waddon, L., & Puzzo, I. (2018). Preliminary evidence on the somatic marker hypothesis applied to investment choices. Journal of Neuroscience, Psychology, and Economics, 11(4), 228–238. https://doi.org/10.1037/npe0000097

Christopoulos, G. I., Uy, M. A., & Yap, W. J. (2019). The Body and the Brain: Measuring Skin Conductance Responses to Understand the Emotional Experience. Organizational Research Methods, 22(1), 394–420. https://doi.org/10.1177/1094428116681073

Crandall, C. G. (2010). Mechanisms and controllers of eccrine sweating in humans. Frontiers in Bioscience, S2(2), 94. https://doi.org/10.2741/s94

Dawson, M. E., Schell, A. M., & Courtney, C. G. (2011). The skin conductance response, anticipation, and decision-making. Journal of Neuroscience, Psychology, and Economics, 4(2), 111–116. https://doi.org/10.1037/a0022619

Gogate, U. D., & Bakal, Dr. J. W. (2019). Hunger and stress monitoring system using galvanic skin response. Indonesian Journal of Electrical Engineering and Computer Science, 13(3), 861. https://doi.org/10.11591/ijeecs.v13.i3.pp861-865

Greco, A., Valenza, G., Lanata, A., Scilingo, E. P., & Citi, L. (2016). CvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering, 63(4), 797–804. https://doi.org/10.1109/TBME.2015.2474131

Greco, A., Valenza, G., & Scilingo, E. P. (2016). Advances in Electrodermal Activity Processing with Applications for Mental Health. Dalam Advances in Electrodermal Activity Processing with Applications for Mental Health: From Heuristic Methods to Convex Optimization. Springer International Publishing. https://doi.org/10.1007/978-3-319-46705-4

Hernando-Gallego, F., Luengo, D., & Artes-Rodriguez, A. (2018). Feature Extraction of Galvanic Skin Responses by Nonnegative Sparse Deconvolution. IEEE Journal of Biomedical and Health Informatics, 22(5), 1385–1394. https://doi.org/10.1109/JBHI.2017.2780252

Hogervorst, M. A., Brouwer, A.-M., & van Erp, J. B. F. (2014). Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload. Frontiers in Neuroscience, 8. https://doi.org/10.3389/fnins.2014.00322

Horvers, A., Tombeng, N., Bosse, T., Lazonder, A. W., & Molenaar, I. (2021). Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review. Sensors (Basel, Switzerland), 21(23). https://doi.org/10.3390/S21237869

Indrayanti, I., Claudia, A. V., Adi, S. P., & Lufityanto, G. (2022). Science Majoring Background Modulates the Psychological Responses to Stress on Numerical Task. Gadjah Mada Journal of Psychology (GamaJoP), 8(2), 150. https://doi.org/10.22146/gamajop.72911

Khakim, Z., & Kusrohmaniah, S. (2022). The Effect of Emotional Distraction on Declarative Memory and an Exploration of its Physiological Marker: An Affective Computing Perspective. Jurnal Psikologi, 49(3), 255. https://doi.org/10.22146/jpsi.74145

Kobas, B., Koth, S. C., Nkurikiyeyezu, K., Giannakakis, G., & Auer, T. (2021). Effect of Exposure Time on Thermal Behaviour: A Psychophysiological Approach. Signals, 2(4), 863–885. https://doi.org/10.3390/signals2040050

Kuoppa, P., Pulkkinen, K., Tarvainen, M. P., Lankinen, M., Lapveteläinen, A., Sinikallio, S., Karhunen, L., Karjalainen, P. A., Kolehmainen, M., Sallinen, J., & Närväinen, J. (2016). Psychophysiological responses to positive and negative food and nonfood visual stimuli. Journal of Neuroscience, Psychology, and Economics, 9(2), 78–88. https://doi.org/10.1037/npe0000053

Mueller, S. Q., Ring, P., & Fischer, M. (2022). Excited and aroused: The predictive importance of simple choice process metrics. Journal of Neuroscience, Psychology, and Economics, 15(1), 31–53. https://doi.org/10.1037/npe0000151

National Research Council. (2003). The Polygraph and Lie Detection. National Academies Press. https://doi.org/10.17226/10420

Nourbakhsh, N., Wang, Y., Chen, F., & Calvo, R. A. (2012). Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks. Proceedings of the 24th Australian Computer-Human Interaction Conference, OzCHI 2012, 420–423. https://doi.org/10.1145/2414536.2414602

Ohira, H., & Hirao, N. (2015). Analysis of skin conductance response during evaluation of preferences for cosmetic products. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00103

Persson, E., Asutay, E., Hagman, W., Västfjäll, D., & Tinghög, G. (2018). Affective response predicts risky choice for fast, but not slow, decisions. Journal of Neuroscience, Psychology, and Economics, 11(4), 213–227. https://doi.org/10.1037/npe0000096

Pop-Jordanova, N., & Pop-Jordanov, J. (2020). Electrodermal Activity and Stress Assessment. Prilozi (Makedonska akademija na naukite i umetnostite. Oddelenie za medicinski nauki), 41(2), 5–15. https://doi.org/10.2478/PRILOZI-2020-0028

Posada-Quintero, H. F., & Chon, K. H. (2020). Innovations in electrodermal activity data collection and signal processing: A systematic review. Sensors (Switzerland), 20(2). https://doi.org/10.3390/S20020479

Rahma, O., Putra, A., Rahmatillah, A., Putri, Y., Fajriaty, N., Ain, K., & Chai, R. (2022). Electrodermal Activity for Measuring Cognitive and Emotional Stress Level. Journal of medical signals and sensors, 12(2), 155–162. https://doi.org/10.4103/JMSS.JMSS_78_20

Ramsøy, T. Z., Jacobsen, C., Friis-Olivarius, M., Bagdziunaite, D., & Skov, M. (2017). Predictive value of body posture and pupil dilation in assessing consumer preference and choice. Journal of Neuroscience, Psychology, and Economics, 10(2–3), 95–110. https://doi.org/10.1037/npe0000073

Reid, C., Keighrey, C., Murray, N., Dunbar, R., & Buckley, J. (2020). A Novel Mixed Methods Approach to Synthesize EDA Data with Behavioral Data to Gain Educational Insight. Sensors 2020, Vol. 20, Page 6857, 20(23), 6857. https://doi.org/10.3390/S20236857

Romine, W., Schroeder, N., Banerjee, T., & Graft, J. (2022). Toward Mental Effort Measurement Using Electrodermal Activity Features. Sensors 2022, Vol. 22, Page 7363, 22(19), 7363. https://doi.org/10.3390/S22197363

Sanchez-Comas, A., Synnes, K., Molina-Estren, D., Troncoso-Palacio, A., & Comas-González, Z. (2021). Correlation Analysis of Different Measurement Places of Galvanic Skin Response in Test Groups Facing Pleasant and Unpleasant Stimuli. Sensors, 21(12), 4210. https://doi.org/10.3390/s21124210

Sapolsky, R. M. (2015). Stress and the brain: Individual variability and the inverted-U. Nature Neuroscience, 18(10), 1344–1346. https://doi.org/10.1038/nn.4109

Sapolsky, R. M. (2021). Glucocorticoids, the evolution of the stress-response, and the primate predicament. Neurobiology of Stress, 14(March), 100320. https://doi.org/10.1016/j.ynstr.2021.100320

Sarchiapone, M., Gramaglia, C., Iosue, M., Carli, V., Mandelli, L., Serretti, A., Marangon, D., & Zeppegno, P. (2018). The association between electrodermal activity (EDA), depression and suicidal behaviour: A systematic review and narrative synthesis. BMC psychiatry, 18(1). https://doi.org/10.1186/S12888-017-1551-4

Setyohadi, D. B., Kusrohmaniah, S., Gunawan, S. B., Pranowo, P., & Prabuwono, A. S. (2018). Galvanic Skin Response Data Classification for Emotion Detection. International Journal of Electrical and Computer Engineering (IJECE), 8(5), 4004. https://doi.org/10.11591/ijece.v8i5.pp4004-4014

Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., & Ehlert, U. (2010). Discriminating Stress From Cognitive Load Using a Wearable EDA Device. IEEE Transactions on Information Technology in Biomedicine, 14(2), 410–417. https://doi.org/10.1109/TITB.2009.2036164

Shaw, S. D., & Bagozzi, R. P. (2018). The neuropsychology of consumer behavior and marketing. Consumer Psychology Review, 1(1), 22–40. https://doi.org/10.1002/arcp.1006

Stagg, S. D., Davis, R., & Heaton, P. (2013). Associations between language development and skin conductance responses to faces and eye gaze in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 43(10), 2303–2311. https://doi.org/10.1007/S10803-013-1780-4/FIGURES/3

Stržinar, Ž., Sanchis, A., Ledezma, A., Sipele, O., Pregelj, B., & Škrjanc, I. (2023). Stress Detection Using Frequency Spectrum Analysis of Wrist-Measured Electrodermal Activity. Sensors 2023, Vol. 23, Page 963, 23(2), 963. https://doi.org/10.3390/S23020963

Stuldreher, I. V, Thammasan, N., van Erp, J. B. F., & Brouwer, A.-M. (2020). Physiological synchrony in EEG, electrodermal activity and heart rate reflects shared selective auditory attention. Journal of Neural Engineering, 17(4), 046028. https://doi.org/10.1088/1741-2552/aba87d

Terriault, P., Kozanitis, A., & Farand, P. (2021). Use of Electrodermal Wristbands to Measure Student’s Cognitive Engagement in the Classroom. Proceedings of the Canadian Engineering Education Association (CEEA). https://doi.org/10.24908/pceea.vi0.14827

Thammasan, N., Stuldreher, I. V., Schreuders, E., Giletta, M., & Brouwer, A.-M. (2020). A Usability Study of Physiological Measurement in School Using Wearable Sensors. Sensors, 20(18), 5380. https://doi.org/10.3390/s20185380

Wincewicz-Cichecka, K., & Nasierowski, T. (2020). Electrodermal activity and suicide risk assessment in patients with affective disorders. Psychiatria polska, 54(6), 1137–1147. https://doi.org/10.12740/PP/110144

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