This book has unveiled the strong relationship between Electrodermal Activity (EDA) signal and autonomic nervous system (ANS) dynamics, and how EDA could be source of reliable and effective markers for the characterization of the physiological response to different emotional stimuli and for the automatic affective and mood state recognition.


Mood State Bipolar Patient Arousal Level Tonic Component Isovaleric Acid 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of neuroscience methods, 190(1), 80–91.CrossRefPubMedPubMedCentralGoogle Scholar
  2. [8]
    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.CrossRefPubMedGoogle Scholar
  3. [10]
    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
  4. [15]
    Boucsein, W. (2012). Electrodermal activity (2nd ed). New York: Springer Science & Business Media.CrossRefGoogle Scholar
  5. [16]
    Benedek, M., & Kaernbach, C. (2010). Decomposition of skin conductance data by means of nonnegative deconvolution. Psychophysiology, 47(4), 647–658.PubMedPubMedCentralGoogle Scholar
  6. [23]
    Boucsein, W. (1992). Electrodermal activity (2nd ed.). New York: Springer Science & Business Media.CrossRefGoogle Scholar
  7. [40]
    Martinsen, Ø., Grimnes, S., & Sveen, O. (1997). Dielectric properties of some keratinised tissues. part 1: Stratum corneum and nail in situ. Medical and Biological Engineering and Computing, 35(3), 172–176.CrossRefPubMedGoogle Scholar
  8. [42]
    Carbonaro, N., Greco, A., Anania, G., Dalle Mura, G., Tognetti, A., Scilingo, E., et al. (2012). Unobtrusive physiological and gesture wearable acquisition system: A preliminary study on behavioral and emotional correlations. Global Health, 1, 88–92.Google Scholar
  9. [57]
    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.CrossRefPubMedGoogle Scholar
  10. [66]
    Bach, D. R. (2014). A head-to-head comparison of SCRalyze and Ledalab, two model-based methods for skin conductance analysis. Biological Psychology, 103, 63–68.CrossRefPubMedPubMedCentralGoogle Scholar
  11. [69]
    Greco, A., Valenza, G., Lanata, A., Scilingo, E., & Citi, L. (2016). cvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering, 63(4), 797–804.PubMedGoogle Scholar
  12. [79]
    Vogelstein, J. T., Packer, A. M., Machado, T. A., Sippy, T., Babadi, B., Yuste, R. et al. (2010). Fast nonnegative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology, 104(6), 3691–3704.CrossRefPubMedPubMedCentralGoogle Scholar
  13. [82]
    de Rooi, J., & Eilers, P. (2011). Deconvolution of pulse trains with the L0 penalty. Analytica Chimica Acta, 705(1), 218–226.CrossRefPubMedGoogle Scholar
  14. [85]
    Posada-Quintero, H. F., Florian, J. P., Orjuela-Cañón, A. D., Aljama-Corrales, T., Charleston-Villalobos, S., & Chon, K. H. (2016). Power spectral density analysis of electrodermal activity for sympathetic function assessment. Annals of Biomedical Engineering, 44, 1–12.CrossRefGoogle Scholar
  15. [157]
    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
  16. [211]
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  17. [212]
    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.CrossRefPubMedGoogle Scholar
  18. [247]
    Bianchi, M., Valenza, G., Serio, A., Lanata, A., Greco, A., Nardelli, M., et al. (2014). Design and preliminary affective characterization of a novel fabric-based tactile display. In 2014 IEEE Haptics Symposium (HAPTICS) (pp. 591–596). IEEE.Google Scholar
  19. [316]
    Yousem, D. M., Maldjian, J. A., Siddiqi, F., Hummel, T., Alsop, D. C., Geckle, R. J., et al. (1999). Gender effects on odor-stimulated functional magnetic resonance imaging. Brain Research, 818(2), 480–487.CrossRefPubMedGoogle Scholar
  20. [321]
    Meli, L., Scheggi, S., Pacchierotti, C., & Prattichizzo, D. (2014). Wearable haptics and hand tracking via an RGB-d camera for immersive tactile experiences. In ACM SIGGRAPH 2014 Posters (p. 56). ACM.Google Scholar
  21. [322]
    Mørkrid, L., & Qiao, Z.-G. (1988). Continuous estimation of parameters in skin electrical admittance from simultaneous measurements at two different frequencies. Medical and Biological Engineering and Computing, 26(6), 633–640.CrossRefPubMedGoogle Scholar
  22. [323]
    Sawan, M., Laaziri, Y., Mounaim, F., Elzayat, E., Corcos, J., & Elhilali, M. (2007). Electrode–tissues interface: Modeling and experimental validation. Biomedical Materials, 2(1), S7.CrossRefPubMedGoogle Scholar
  23. [324]
    Pavšelj, N., Préat, V., & Miklavčič, D. (2007). A numerical model of skin electropermeabilization based on in vivo experiments. Annals of Biomedical Engineering, 35(12), 2138–2144.CrossRefPubMedGoogle Scholar
  24. [325]
    Empatica: Human data in real time. (2016).
  25. [326]
    Fernandez, R., & Picard, R. W. (2003). Modeling drivers’ speech under stress. Speech Communication, 40(1), 145–159.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

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

Personalised recommendations