Cited by 526Heart Rate Variability and Autonomic ControlEmotion and Mood RecognitionNon-Invasive Vital Sign Monitoring

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Innovations in Electrodermal Activity Data Collection and Signal Processing: A Systematic Review

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Posada-Quintero, Hugo F., Chon, Ki H.·2020·Sensors

DOI 10.3390/s20020479PMC PMC7014446

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Abstract

The electrodermal activity (EDA) signal is an electrical manifestation of the sympathetic innervation of the sweat glands. EDA has a history in psychophysiological (including emotional or cognitive stress) research since 1879, but it was not until recent years that researchers began using EDA for pathophysiological applications like the assessment of fatigue, pain, sleepiness, exercise recovery, diagnosis of epilepsy, neuropathies, depression, and so forth. The advent of new devices and applications for EDA has increased the development of novel signal processing techniques, creating a growing pool of measures derived mathematically from the EDA. For many years, simply computing the mean of EDA values over a period was used to assess arousal. Much later, researchers found that EDA contains information not only in the slow changes (tonic component) that the mean value represents, but also in the rapid or phasic changes of the signal. The techniques that have ensued have intended to provide a more sophisticated analysis of EDA, beyond the traditional tonic/phasic decomposition of the signal. With many researchers from the social sciences, engineering, medicine, and other areas recently working with EDA, it is timely to summarize and review the recent developments and provide an updated and synthesized framework for all researchers interested in incorporating EDA into their research.

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Figure 1 EDA signal and an isolated SCR.CC BY · PubMed Central

Figure 2 A typical skin conductance response (SCR) and illustration of some derived measures.CC BY · PubMed Central

Figure 3 EDA data decomposition into tonic and phasic components.CC BY · PubMed Central

Figure 4 Tonic/phasic decomposition of a sample EDA signal using some available tools.CC BY · PubMed Central

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Electrodermal Activity
Boucsein, W. (2012) · doi:10.1007/978-1-4614-1126-0 · Cited by 2,526
Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
Healey, J. A., & Picard, R. W. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2005.848368 · Cited by 2,207
The Electrodermal System
Dawson, M. E., Schell, A. M., Filion, D. L., & Berntson, G. G. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511546396.007 · Cited by 1,776
Publication Recommendations for Electrodermal Measurements
Fowles, D. C., Christie, M. J., Edelberg, R., Grings, W. W., Lykken, D. T., & Venables, P. H. (1981) · Psychophysiology · doi:10.1111/j.1469-8986.1981.tb03024.x · Cited by 1,700
A continuous measure of phasic electrodermal activity
Benedek, M., & Kaernbach, C. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.04.028 · Cited by 1,689
Publication recommendations for electrodermal measurements
Society for Psychophysiological Research Ad Hoc Committee on Electrodermal Measures (2012) · Psychophysiology · doi:10.1111/j.1469-8986.2012.01384.x · Cited by 1,288
Discriminating Stress From Cognitive Load Using a Wearable EDA Device
Setz, C., Arnrich, B., Schumm, J., La Marca, R., Troster, G., & Ehlert, U. (2010) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2009.2036164 · Cited by 742
A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity
Poh, M.-Z., Swenson, N. C., & Picard, R. W. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2038487 · Cited by 693
cvxEDA: a Convex Optimization Approach to Electrodermal Activity Processing
Greco, A., Valenza, G., Lanatà, A., Scilingo, E. P., & Citi, L. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2474131 · Cited by 646
Decomposition of skin conductance data by means of nonnegative deconvolution
Benedek, M., & Kaernbach, C. (2010) · Psychophysiology · doi:10.1111/j.1469-8986.2009.00972.x · Cited by 594

Show all 75 references

Research works printed but not linked: 5

Cacioppo, J.T., Tassinary, L.G., and Berntson, G. (2007). Handbook of Psychophysiology, Cambridge University Press. [3rd ed.].
Martin, I., and Venables, P.H. (1980). Techniques in Psychophysiology, John Wiley & Sons. Psychological Medicine.
Hugdahl, K. (1995). Psychophysiology: The Mind–Body Perspective, Harvard University Press.
Grings, 1974, Recording of electrodermal phenomena, Methods Physiol. Psychol., 1, 273
Boucsein, W. (2012). Electrodermal Activity, Springer Science & Business Media. · DOI · Paper page

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Authors: Posada-Quintero, H. F., & Chon, K. H.
Affiliations: University of Connecticut (US)
Corresponding author: Chon, Ki H.
ORCID: Posada-Quintero, Hugo F.
Chon, Ki H.
Venue: Sensors
Publisher: MDPI AG
Published: 2020-01-15
Volume: 20
Issue: 2
Pages: 479
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s20020479
PMC: PMC7014446
OpenAlex: W3000402017
Cited by: 525
References: 75
Citations by year: 2026: 103 · 2025: 92 · 2024: 109 · 2023: 85 · 2022: 57 · 2021: 57 · 2020: 22
FWCI: 9.90
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sensors (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Office of Naval Research (N00014-19-1-2209)
Topics: Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
Keywords: electrodermal activity, sympathetic nervous system, sweat glands, psychophysiology, fatigue analysis, pain assessment, sleepiness, post-exercise recovery, epilepsy diagnosis, neuropathy, depression

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