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      A Review of Emotion Recognition Using Physiological Signals

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          Abstract

          Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

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          Autonomic nervous system activity in emotion: A review

          Sylvia D Kreibig (2010)
          Autonomic nervous system (ANS) activity is viewed as a major component of the emotion response in many recent theories of emotion. Positions on the degree of specificity of ANS activation in emotion, however, greatly diverge, ranging from undifferentiated arousal, over acknowledgment of strong response idiosyncrasies, to highly specific predictions of autonomic response patterns for certain emotions. A review of 134 publications that report experimental investigations of emotional effects on peripheral physiological responding in healthy individuals suggests considerable ANS response specificity in emotion when considering subtypes of distinct emotions. The importance of sound terminology of investigated affective states as well as of choice of physiological measures in assessing ANS reactivity is discussed. Copyright © 2010 Elsevier B.V. All rights reserved.
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            Are Emotions Natural Kinds?

            Lisa Feldman Barrett (2016)
            Laypeople and scientists alike believe that they know anger, or sadness, or fear, when they see it. These emotions and a few others are presumed to have specific causal mechanisms in the brain and properties that are observable (on the face, in the voice, in the body, or in experience)-that is, they are assumed to be natural kinds. If a given emotion is a natural kind and can be identified objectively, then it is possible to make discoveries about that emotion. Indeed, the scientific study of emotion is founded on this assumption. In this article, I review the accumulating empirical evidence that is inconsistent with the view that there are kinds of emotion with boundaries that are carved in nature. I then consider what moving beyond a natural-kind view might mean for the scientific understanding of emotion.
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              The emotion probe. Studies of motivation and attention.

              P J Lang (1995)
              Emotions are action dispositions--states of vigilant readiness that vary widely in reported affect, physiology, and behavior. They are driven, however, by only 2 opponent motivational systems, appetitive and aversive--subcortical circuits that mediate reactions to primary reinforcers. Using a large emotional picture library, reliable affective psychophysiologies are shown, defined by the judged valence (appetitive/pleasant or aversive/unpleasant) and arousal of picture percepts. Picture-evoked affects also modulate responses to independently presented startle probe stimuli. In other words, they potentiate startle reflexes during unpleasant pictures and inhibit them during pleasant pictures, and both effects are augmented by high picture arousal. Implications are elucidated for research in basic emotions, psychopathology, and theories of orienting and defense. Conclusions highlight both the approach's constraints and promising paths for future study.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 28 June 2018
                Publication date Collection: July 2018
                Volume: 18
                Issue: 7
                Electronic Location Identifier: 2074
                Affiliations
                School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China; shul@ 123456scut.edu.cn (L.S.); xie.jinyan@ 123456mail.scut.edu.cn (J.X.); eemingyueyang@ 123456mail.scut.edu.cn (M.Y.); eve.llzzyy@ 123456gmail.com (Z.L.); li.zhenqi@ 123456mail.scut.edu.cn (Z.L.); danl@ 123456scut.edu.cn (D.L.); cindy.yang1001@ 123456gmail.com (X.Y.)
                Author notes
                [* ]Correspondence: xmxu@ 123456scut.edu.cn ; Tel: +86-20-22236158
                Article
                Publisher ID: sensors-18-02074
                DOI: 10.3390/s18072074
                PMC ID: 6069143
                PubMed ID: 29958457
                SO-VID: 9628c3d5-7582-438e-9ad7-68bf93e5eb15
                Copyright © © 2018 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 10 April 2018
                Date accepted : 12 June 2018
                Categories
                Subject: Review

                ScienceOpen disciplines: Biomedical engineering
                Keywords: emotion recognition,physiological signals,emotion model,emotion stimulation,features,classifiers
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: emotion recognition, physiological signals, emotion model, emotion stimulation, features, classifiers

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