Skip to main content
SpringerLink
Log in
Book cover

Advances in Electrodermal Activity Processing with Applications for Mental Health pp 45–54Cite as

Emotions and Mood States: Modeling, Elicitation, and Recognition

  • Chapter
  • First Online:

Abstract

In this chapter, we introduce basic concepts related to the theory of emotions, as well as the strict link between emotions and mood/mental disorders. Then, ANS correlates of emotions and mood disorders, with a special emphasis on EDA, will also be reported. This knowledge backgrounds the experimental applications described in details in the Chap. 5

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Bibliography

  1. Valenza, G., Gentili, C., Lanata, A., & Scilingo, E. (2013). Mood recognition in bipolar patients through the psyche platform: preliminary evaluations and perspectives. Artificial Intelligence In Medicine, 57(1), 49–58.

    Article  PubMed  Google Scholar 

  2. Vanello, N., Guidi, A., Gentili, C., Werner, S., Bertschy, G., Valenza, G., et al. (2012). Speech analysis for mood state characterization in bipolar patients. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2104–2107). IEEE.

    Google Scholar 

  3. Boucsein, W. (1992). Electrodermal activity (2nd ed.). New York: Springer Science & Business Media.

    Book  Google Scholar 

  4. Lanata, A., Valenza, G., & Scilingo, E. (2012). A novel EDA glove based on textile-integrated electrodes for affective computing. Medical and Biological Engineering and Computing, 50, 1163–1172.

    Article  PubMed  Google Scholar 

  5. Lang, P., Bradley, M., & Cuthbert, B. (2005). International affective picture system iaps): Digitized photographs, instruction manual and affective ratings. Technical Report A-6. University of Florida.

    Google Scholar 

  6. Frijda, N. (1986). The emotions. Cambridge: Cambridge University Press.

    Google Scholar 

  7. Watson, D., & Clark, L. (1992). On traits and temperament: General and specific factors of emotional experience and their relation to the five-factor model. Journal of Personality, 60(2), 441–476.

    Article  CAS  PubMed  Google Scholar 

  8. LeDoux, J. (1998). The emotional brain: The mysterious underpinnings of emotional life. New York: Simon and Schuster.

    Google Scholar 

  9. Pegna, A. J., Khateb, A., Lazeyras, F., & Seghier, M. L. (2005). Discriminating emotional faces without primary visual cortices involves the right amygdala. Nature Neuroscience, 8(1), 24–25.

    Article  CAS  PubMed  Google Scholar 

  10. Ekman, P. (1993). Facial expression and emotion. American psychologist, 48(4), 384.

    Article  CAS  PubMed  Google Scholar 

  11. Darwin, C. (1872). The expression of the emotions in man and animals; with an introduction, afterword, and commentaries by Paul Ekman. New York: Oxford University.

    Google Scholar 

  12. Ekman, P. (1974). 1. universal facial expressions of emotion. In R. A. LeVine (Ed.), Culture and personality: Contemporary readings (pp. 8–15). Chicago: Aldine.

    Google Scholar 

  13. Ekman, P. (1999). Basic emotions. In T. Dalgleish, & T. Power (Eds.), The Handbook of cognition and emotion, (pp. 45–60). Sussex: Wiley.

    Google Scholar 

  14. Tompkins, S. (1962). Affect Imagery Consciousness: Volume I: The Positive Affects. New York: Springer Publishing Company.

    Google Scholar 

  15. Plutchik, R. (1984). Emotions: A general psychoevolutionary theory. Approaches to Emotion, 197–219.

    Google Scholar 

  16. Ekman, P. (1973). Cross-cultural studies of facial expression. In P. Ekman (Ed.), Darwin and facial expression: A century of research in review, (pp. 169–222). New York: Academic.

    Google Scholar 

  17. Watson. J. (1997) Behaviorism. New Brunswick: Transaction Publication.

    Google Scholar 

  18. Ortony, A., & Turner, T. (1990). What is basic about basic emotions. Psychological Review, 97(3), 315–331.

    Article  CAS  PubMed  Google Scholar 

  19. Wundt, W. (1905). Grundriss der psychologie [Fundamentals of psychology]. (7th rev. ed.). Liepzig: Engelman.

    Google Scholar 

  20. Schlosberg, H. (1954). Three dimensions of emotion. Psychological Review, 61(2), 81–88.

    Article  CAS  PubMed  Google Scholar 

  21. Breazeal, C. (2003). Emotion and sociable humanoid robots. International Journal of Human-Computer Studies, 59(1–2), 119–155.

    Article  Google Scholar 

  22. Russell, J. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178.

    Article  Google Scholar 

  23. Russell, J., & Mehrabian, A. (1977). Evidence for a three-factor theory of emotions* 1. Journal of Research in Personality, 11(3), 273–294.

    Article  Google Scholar 

  24. Arnold, M. B. (1950). An excitatory theory of emotion. In M. L. Reymert (Ed.), Feelings and emotions (pp. 11–33). New York: McGraw-Hill.

    Google Scholar 

  25. Ortony, A., Clore, G., & Collins, A. (1990). The cognitive structure of emotions. Cambridge: Cambridge University Press.

    Google Scholar 

  26. Scherer, K., & Ekman, P. (1984). Approaches to emotions. Londres et New Jersey: Lawrence Erlbaum Associates, Publication.

    Google Scholar 

  27. Lisetti, C., & Gmytrasiewicz, P. (2002). Can a rational agent afford to be affectless? A formal approach. Applied Artificial Intelligence, 16, 1–33.

    Article  Google Scholar 

  28. Scherer, K., Schorr, A., & Johnstone, T. (2001). Appraisal processes in emotion: Theory, methods, research. Oxford: Oxford University Press.

    Google Scholar 

  29. Egges, A., Kshirsagar, S., & Magnenat-Thalmann, N. (2003). A model for personality and emotion simulation. In Knowledge-based intelligent information and engineering systems (pp. 453–461). Berlin: Springer.

    Chapter  Google Scholar 

  30. Posner, J., Russell, J., & Peterson, B. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17(03), 715–734.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Nasoz, F., Alvarez, K., Lisetti, C., & Finkelstein, N. (2003). Emotion recognition from physiological signals using wireless sensors for presence technologies. Cognition, Technology and Work, 6, 4–14.

    Article  Google Scholar 

  32. Janig, W. (1989). Autonomic nervous system. In R. F. Schmidt, & G. Thews (Eds.), Human physiology, (2nd ed.). Berlin: Springer.

    Google Scholar 

  33. Lewis, M., Haviland-Jones, J. M., & Barrett, L. F. (2010). Handbook of emotions. New York: Guilford Press.

    Google Scholar 

  34. Picard, R. (2000). Affective computing. Cambridge: MIT.

    Google Scholar 

  35. Picard, R., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175–1191.

    Article  Google Scholar 

  36. Kim, J., & André, E. (2008). Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), 2067–2083.

    Article  PubMed  Google Scholar 

  37. Katsis, C. D., Katertsidis, N. S., & Fotiadis, D. I. (2010). An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. Biomedical Signal Processing and Control, 6(3), 261–268.

    Article  Google Scholar 

  38. Lanatá, A., Valenza, G., Mancuso, C., & Scilingo, E. (2011). Robust multiple cardiac arrhythmia detection through bispectrum analysis. Expert Systems with Applications, 38(6), 6798–6804.

    Article  Google Scholar 

  39. Valenza, G., & Scilingo, E. P. (2014) Autonomic nervous system dynamics for mood and emotional-state recognition. Springer.

    Book  Google Scholar 

  40. James, W. (1884). II.—what is an emotion? Mind, os-IX (34), 188–205. doi:10.1093/ mind/os-IX.34.188.

    Google Scholar 

  41. Ellsworth, P. C. (1994). William James and emotion: is a century of fame worth a century of misunderstanding? Psychological Review, 101(2), 222.

    Article  CAS  PubMed  Google Scholar 

  42. Lang, P. J. (1994). The varieties of emotional experience: A meditation on james-lange theory. Psychological Review, 101(2), 211.

    Article  CAS  PubMed  Google Scholar 

  43. Scherer, K. R., & Wallbott, H. G. (1994). Evidence for universality and cultural variation of differential emotion response patterning. Journal of Personality and Social Psychology, 66(2), 310.

    Article  CAS  PubMed  Google Scholar 

  44. Stemmler, G., Heldmann, M., Pauls, C. A., & Scherer, T. (2001). Constraints for emotion specificity in fear and anger: The context counts. Psychophysiology, 38(2), 275–291.

    Article  CAS  PubMed  Google Scholar 

  45. Christie, I., & Friedman, B. (2004). Autonomic specificity of discrete emotion and dimensions of affective space: A multivariate approach. International Journal of Psychophysiology, 51(2), 143–153.

    Article  PubMed  Google Scholar 

  46. Cacioppo, J. T., Berntson, G. G., Larsen, J. T., Poehlmann, K. M., Ito, T. A., et al. (2000). The psychophysiology of emotion. Handbook of Emotions, 2, 173–191.

    Google Scholar 

  47. Arnold, M. B. (1960). Emotion and personality. New York: Columbia University.

    Google Scholar 

  48. Hillman, J. (1960). Emotion: a comprehensive phenomenology of theories and their meaning for therapy. Evanston: Northwestern University Press.

    Google Scholar 

  49. Campos, J. J., & Johnson, H. J. (1967). Affect, verbalization, and directional fractionation of autonomic responses. Psychophysiology, 3(3), 285–290.

    Article  CAS  PubMed  Google Scholar 

  50. Bradley, M. M., & Lang, P. J. (2000). Measuring emotion: Behavior, feeling, and physiology. Cognitive Neuroscience of Emotion, 25, 49–59.

    Google Scholar 

  51. Stemmler, G. (2004). Physiological processes during emotion. In P. Philippot & R. S. Feldman (Eds.), The regulation of emotion (pp. 33–70). Mahwah: Erlbaum.

    Google Scholar 

  52. Calvo, R., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37.

    Article  Google Scholar 

  53. Greco, A., Valenza, G., Nardelli, M., Lanata, A., Bianchi, M., & Scilingo, E. P. (2015). Electrodermal activity analysis during affective haptic elicitation. In 2015 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE.

    Google Scholar 

  54. Swangnetr, M., & Kaber, D. B. (2012). Emotional state classification in patient–robot interaction using wavelet analysis and statistics-based feature selection. IEEE Transactions on Systems, Man and Cybernetics, 43(1), 63–75.

    Google Scholar 

  55. Katsis, C., Katertsidis, N., Ganiatsas, G., & Fotiadis, D. (2008). Toward emotion recognition in car-racing drivers: A biosignal processing approach. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 38(3), 502–512.

    Article  Google Scholar 

  56. Lanatà, A., Valenza, G., Greco, A., Gentili, C., Bartolozzi, R., Bucchi, F., et al. (2015). How the autonomic nervous system and driving style change with incremental stressing conditions during simulated driving. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1505–1517.

    Article  Google Scholar 

  57. Chakraborty, A., Konar, A., Chakraborty, U., & Chatterjee, A. (2009). Emotion recognition from facial expressions and its control using fuzzy logic. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 39(4), 726–743.

    Article  Google Scholar 

  58. Chanel, G., Rebetez, C., Bétrancourt, M., & Pun, T. (2011). Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 41(6), 1052–1063.

    Article  Google Scholar 

  59. Lisetti, C., & Nasoz, F. (2004). Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP Journal on Applied Signal Processing, 2004, 1672–1687.

    Article  Google Scholar 

  60. Yoo, S., Lee, C., Park, Y., Kim, N., Lee, B., & Jeong, K. (2005). Neural network based emotion estimation using heart rate variability and skin resistance. In International conference on natural computation. Lecture Notes in Computer Science (Vol. 3610, pp. 818–824). Berlin/Heidelberg: Springer.

    Google Scholar 

  61. Choi, A., & Woo, W. (2005). Physiological sensing and feature extraction for emotion recognition by exploiting acupuncture spots. In International conference on affective computing and intelligent interaction. Lecture Notes in Computer Science (Vol. 3784, pp. 590–597). Berlin/Heidelberg: Springer.

    Google Scholar 

  62. Healey, J., & Picard, R. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6(2), 156–166.

    Article  Google Scholar 

  63. Li, L., & Chen, J. (2006). Emotion recognition using physiological signals. In Advances in artificial reality and tele-existence. Lecture Notes in Computer Science (Vol. 4282, pp. 437–446). Berlin/Heidelberg: Springer.

    Google Scholar 

  64. Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006). An empirical study of machine learning techniques for affect recognition in human–robot interaction. Pattern Analysis & Applications, 9(1), 58–69.

    Article  Google Scholar 

  65. Rainville, P., Bechara, A., Naqvi, N., & Damasio, A. (2006). Basic emotions are associated with distinct patterns of cardiorespiratory activity. International Journal of Psychophysiology, 61(1), 5–18.

    Article  PubMed  Google Scholar 

  66. Zhai, J., & Barreto, A. (2006). Stress detection in computer users based on digital signal processing of noninvasive physiological variables. In 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1355–1358).

    Google Scholar 

  67. Leon, E., Clarke, G., Callaghan, V., & Sepulveda, F. (2007). A user-independent real-time emotion recognition system for software agents in domestic environments. Engineering Applications of Artificial Intelligence, 20(3), 337–345.

    Article  Google Scholar 

  68. Liu, C., Conn, K., Sarkar, N., & Stone, W. (2008). Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder. International Journal of Human-Computer Studies, 66(9), 662–677.

    Article  Google Scholar 

  69. Yannakakis, G., & Hallam, J. (2008). Entertainment modeling through physiology in physical play. International Journal of Human-Computer Studies, 66(10), 741–755.

    Article  Google Scholar 

  70. Gouizi, K., Bereksi Reguig, F., & Maaoui, C. (2011). Emotion recognition from physiological signals. Journal of Medical Engineering & Technology, 35(6–7), 300–307.

    Article  CAS  Google Scholar 

  71. Valenza, G., Lanatà, A., & Scilingo, E. (2011). The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Transactions on Affective Computing. doi:10.1109/T-AFFC.2011.30.

    Google Scholar 

  72. Jang, E.-H., Park, B.-J., Kim, S.-H., & Sohn, J.-H. (2012). Emotion classification based on physiological signals induced by negative emotions: Discriminantion of negative emotions by machine learning algorithm. In 2012 9th IEEE International Conference on Networking, Sensing and Control (ICNSC) (pp. 283–288). IEEE.

    Google Scholar 

  73. Ren, P., Barreto, A., Gao, Y., & Adjouadi, M. (2011). Comparison of the use of pupil diameter and galvanic skin response signals for affective assessment of computer users. Biomedical Sciences Instrumentation, 48, 345–350.

    Google Scholar 

  74. Torres, C. A., Orozco, Á. A., & Alvarez, M. A. (2013). Feature selection for multimodal emotion recognition in the arousal-valence space. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4330–4333). IEEE.

    Google Scholar 

  75. Zhaofang Y., & Guangyuan, L. (2013). Emotion recognition based on nonlinear features of skin conductance response. Journal of Information & Computational Science, 10(12), 3877–3887.

    Article  Google Scholar 

  76. Maaoui, C., Abdat, F., & Pruski, A. (2014). Physio-visual data fusion for emotion recognition. IRBM, 35(3), 109–118.

    Article  Google Scholar 

  77. Kukolja, D., Popović, S., Horvat, M., Kovač, B., & Ćosić, K. (2014). Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications. International Journal of Human-Computer Studies, 72(10), 717–727.

    Article  Google Scholar 

  78. Hariharan, A., & Adam, M. T. P. (2015). Blended emotion detection for decision support. IEEE Transactions on Human-Machine Systems, 45(4), 510–517.

    Article  Google Scholar 

  79. Khezri, M., Firoozabadi, M., & Sharafat, A. R. (2015). Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals. Computer Methods and Programs in Biomedicine, 122(2), 149–164.

    Article  PubMed  Google Scholar 

  80. Khan, A. M., & Lawo, M. (2016). Recognizing emotion from blood volume pulse and skin conductance sensor using machine learning algorithms (pp. 1291–1297). Cham: Springer International Publishing. Available: http://dx.doi.org/10.1007/978-3-319-32703-7_247 [Online].

  81. Rukavina, S., Gruss, S., Hoffmann, H., Tan, J.-W., Walter, S., & Traue, H. C. (2016). Affective computing and the impact of gender and age. PloS One, 11(3), e0150584.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Bialoskorski, L. S., Westerink, J. H., & Broek, E. L. (2009). Mood swings: An affective interactive art system. In International conference on intelligent technologies for interactive entertainment. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Vol. 9, pp. 181–186). Berlin/Heidelberg: Springer.

    Google Scholar 

  83. Lisetti, C. L., & Nasoz, F. (2004). Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP Journal on Advances in Signal Processing, 2004(11), 1–16.

    Article  Google Scholar 

  84. Nardelli, M., Valenza, G., Greco, A., Lanata, A., & Scilingo, E. (2015). Recognizing emotions induced by affective sounds through heart rate variability. IEEE Transactions of Affective Computing, 6(4), 385–394.

    Article  Google Scholar 

  85. Wagner, J., Kim, J., & André, E. (2005). From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. In 2005 IEEE International Conference on Multimedia and Expo (pp. 940–943). IEEE.

    Google Scholar 

  86. Van den Broek, E., Schut, M., Westerink, J., & Tuinenbreijer, K. (2009). Unobtrusive Sensing of Emotions (USE). Journal of Ambient Intelligence and Smart Environments, 1(3), 287–299.

    Google Scholar 

  87. Van den Broek, E., & Westerink, J. (2009). Considerations for emotion-aware consumer products. Applied Ergonomics, 40(6), 1055–1064.

    Article  PubMed  Google Scholar 

  88. Zeng, Z., Pantic, M., Roisman, G., & Huang, T. (2008). A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(1), 39–58.

    Article  Google Scholar 

  89. Poels, K., & Dewitte, S. (2006). How to capture the heart? reviewing 20 years of emotion measurement in advertising. Journal of Advertising Research-New York, 46(1), 18.

    Article  Google Scholar 

  90. Chanel, G., Kierkels, J., Soleymani, M., & Pun, T. (2009). Short-term emotion assessment in a recall paradigm. International Journal of Human-Computer Studies, 67(8), 607–627.

    Article  Google Scholar 

  91. Healey, J. (2009). Affect detection in the real world: Recording and processing physiological signals. In 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009 (ACII 2009) (pp. 1–6). IEEE.

    Google Scholar 

  92. Lang, P., & Bradley, M. (1999). International affective digitized sounds (IADS): Stimuli, 802 instruction manual and affective ratings (Technical Report no. b-2) (Vol. 803). Gainsville.

    Google Scholar 

  93. Bensafi, M. (2001). Le traitement affectif des odeurs: aspects implicites et explicites. Ph.d Thesis in, Lyon 2 University.

    Google Scholar 

  94. Lang, P., Bradley, M., & Cuthbert, B. (1997). International affective picture system (IAPS): Technical manual and affective ratings. Gainesville: The Center for Research in Psychophysiology, University of Florida.

    Google Scholar 

  95. Lang, P., Greenwald, M., Bradley, M., & Hamm, A. (1993). Looking at pictures: Affective, facial, visceral, and behavioral reactions. Psychophysiology, 30(3), 261–273.

    Article  CAS  PubMed  Google Scholar 

  96. Lang, P. (1980). Behavioral treatment and bio-behavioral assessment: Computer applications. Technology in Mental Health Care Delivery Systems, 119–137.

    Google Scholar 

  97. Grimm, S., Schmidt, C., Bermpohl, F., Heinzel, A., Dahlem, Y., Wyss, M., et al. (2006). Segregated neural representation of distinct emotion dimensions in the prefrontal cortex–an fMRI study. Neuroimage, 30(1), 325–340.

    Google Scholar 

  98. Hariri, A., Mattay, V., Tessitore, A., Fera, F., & Weinberger, D. (2003). Neocortical modulation of the amygdala response to fearful stimuli. Biological Psychiatry, 53(6), 494–501.

    Article  PubMed  Google Scholar 

  99. Chatel-Goldman, J., Congedo, M., Jutten, C., & Schwartz, J.-L. (2014). Touch increases autonomic coupling between romantic partners. Frontiers in Behavioral Neuroscience, 8, 95. http://doi.org/10.3389/fnbeh.2014.00095.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Liljencrantz, J., & Olausson, H. (2014). Tactile c fibers and their contributions to pleasant sensations and to tactile allodynia. Frontiers in Behavioral Neuroscience, 8, 37. http://doi.org/10.3389/fnbeh.2014.00037.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Zotterman, Y. (1939). Touch, pain and tickling: An electro-physiological investigation on cutaneous sensory nerves. Journal of Physiology, 95(1), 1–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Rolls, E. T. (2010). The affective and cognitive processing of touch, oral texture, and temperature in the brain. Neuroscience & Biobehavioral Reviews, 34(2), 237–245.

    Article  Google Scholar 

  103. Triscoli, C., Olausson, H., Sailer, U., Ignell, H., & Croy, I. (2013). CT-optimized skin stroking delivered by hand or robot is comparable. Frontiers in Behavioral Neuroscience, 7, 208.

    Article  PubMed  PubMed Central  Google Scholar 

  104. Löken, L. S., Wessberg, J., McGlone, F., & Olausson, H. (2009). Coding of pleasant touch by unmyelinated afferents in humans. Nature Neuroscience, 12(5), 547–548.

    Article  PubMed  Google Scholar 

  105. Kessler, R., McGonagle, K., Zhao, S., Nelson, C., Hughes, M., Eshleman, S., et al. (1994). Lifetime and 12-month prevalence of dsm-iii-r psychiatric disorders in the united states: Results from the national comorbidity survey. Archives of General Psychiatry, 51(1), 8.

    Article  CAS  PubMed  Google Scholar 

  106. Kauer-SantAnna, M., Kapczinski, F., & Vieta, E. (2009). Epidemiology and management of anxiety in patients with bipolar disorder. CNS Drugs, 23(11), 953–964.

    Article  CAS  Google Scholar 

  107. Young, R., Biggs, J., Ziegler, V., & Meyer, D. (1978). A rating scale for mania: Reliability, validity and sensitivity. The British Journal of Psychiatry, 133(5), 429–435.

    Article  CAS  PubMed  Google Scholar 

  108. Vieta, E., Reinares, M., & Rosa, A. (2011). Staging bipolar disorder. Neurotoxicity Research, 19(2), 279–285.

    Article  PubMed  Google Scholar 

  109. Phillips, M., & Vieta, E. (2007). Identifying functional neuroimaging biomarkers of bipolar disorder: Toward dsm-v. Schizophrenia Bulletin, 33(4), 893–904.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Cohen, H., Kaplan, Z., Kotler, M., Mittelman, I., Osher, Y., & Bersudsky, Y. (2003). Impaired heart rate variability in euthymic bipolar patients. Bipolar Disorders, 5(2), 138–143.

    Article  PubMed  Google Scholar 

  111. Valenza, G., Nardelli, M., Lanata, A., Gentili, C., Bertschy, G., Paradiso, R., et al., Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE Journal of Biomedical and Health Informatics, 18(5), 1625–1635.

    Google Scholar 

  112. Iverson, G., Gaetz, M., Rzempoluck, E., McLean, P., Linden, W., & Remick, R. (2005). A new potential marker for abnormal cardiac physiology in depression. Journal of Behavioral Medicine, 28(6), 507–511.

    Article  PubMed  Google Scholar 

  113. Taillard, J., Lemoine, P., Boule, P., Drogue, M., & Mouret, J. (1993). Sleep and heart rate circadian rhythm in depression: The necessity to separate. Chronobiology International, 10(1), 63–72.

    Article  CAS  PubMed  Google Scholar 

  114. Taillard, J., Sanchez, P., Lemoine, P., & Mouret, J. (1990). Heart rate orcadian rhythm as a biological marker of desynchronization in major depression: A methodological and preliminary report. Chronobiology International, 7(4), 305–316.

    Article  CAS  PubMed  Google Scholar 

  115. Iacono, W. G., & Tuason, V. B. (1983). Bilateral electrodermal asymmetry in euthymic patients with unipolar and bipolar affective disorders. Biological Psychiatry, 18(3), 303–315.

    CAS  PubMed  Google Scholar 

  116. Iacono, W. G., Lykken, D. T., Peloquin, L. J., Lumry, A. E., Valentine, R. H., & Tuason, V. B. (1983). Electrodermal activity in euthymic unipolar and bipolar affective disorders: A possible marker for depression. Archives of General Psychiatry, 40(5), 557.

    Article  CAS  PubMed  Google Scholar 

  117. Sponheim, S., Allen, J., & Iacono, W. (1995). Selected psychophysiological measures in depression: The significance of electrodermal activity, electroencephalographic asymmetries, and contingent negative variation to behavioral and neurobiological aspects of depression. The behavioral high risk paradigm in psychopathology (pp. 222–249). New York: Springer.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Engineering, Bioengineering and Robotics Research Center “E. Piaggio”, University of Pisa, Pisa, Italy

    Alberto Greco, Gaetano Valenza & Enzo Pasquale Scilingo

Authors
  1. Alberto Greco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaetano Valenza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Enzo Pasquale Scilingo
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this chapter

Cite this chapter

Greco, A., Valenza, G., Scilingo, E.P. (2016). Emotions and Mood States: Modeling, Elicitation, and Recognition. In: Advances in Electrodermal Activity Processing with Applications for Mental Health. Springer, Cham. https://doi.org/10.1007/978-3-319-46705-4_4

Download citation

Publish with us

Policies and ethics

Search

Navigation