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Advances in Electrodermal Activity Processing with Applications for Mental Health pp 111–121Cite as

Conclusions

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Abstract

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.

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Bibliography

  1. Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of neuroscience methods, 190(1), 80–91.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 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 

  3. 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. Boucsein, W. (2012). Electrodermal activity (2nd ed). New York: Springer Science & Business Media.

    Book  Google Scholar 

  5. Benedek, M., & Kaernbach, C. (2010). Decomposition of skin conductance data by means of nonnegative deconvolution. Psychophysiology, 47(4), 647–658.

    PubMed  PubMed Central  Google Scholar 

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

    Book  Google Scholar 

  7. 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.

    Article  CAS  PubMed  Google Scholar 

  8. 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. 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 

  10. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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.

    PubMed  Google Scholar 

  12. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  13. de Rooi, J., & Eilers, P. (2011). Deconvolution of pulse trains with the L0 penalty. Analytica Chimica Acta, 705(1), 218–226.

    Article  PubMed  Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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. 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 

  17. 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 

  18. 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. 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.

    Article  CAS  PubMed  Google Scholar 

  20. 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. 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.

    Article  PubMed  Google Scholar 

  22. 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.

    Article  CAS  PubMed  Google Scholar 

  23. 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.

    Article  PubMed  Google Scholar 

  24. Empatica: Human data in real time. https://www.empatica.com/ (2016).

  25. Fernandez, R., & Picard, R. W. (2003). Modeling drivers’ speech under stress. Speech Communication, 40(1), 145–159.

    Article  Google Scholar 

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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
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  2. Gaetano Valenza
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  3. Enzo Pasquale Scilingo
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Greco, A., Valenza, G., Scilingo, E.P. (2016). Conclusions. In: Advances in Electrodermal Activity Processing with Applications for Mental Health. Springer, Cham. https://doi.org/10.1007/978-3-319-46705-4_6

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