1. Blood, J. D., Wu, J., Chaplin, T. M., Hommer, R., Vazquez, L., Rutherford, H. J. & Crowley, M. J. (2015). The variable heart: high frequency and very low frequency correlates of depressive symptoms in children and adolescents. Journal of affective disorders, 186, 119-126.
  2. Cai, J., Liu, G., & Hao, M. (2009, July). The research on emotion recognition from ECG signal. In Information Technology and Computer Science, 2009. ITCS 2009. International Conference on (Vol. 1, pp. 497-500). IEEE.
  3. Valderas, M. T., Bolea, J., Laguna, P., Vallverdú, M., & Bailón, R. (2015, August). Human emotion recognition using heart rate variability analysis with spectral bands based on respiration. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 6134-6137). IEEE.
  4. Ferdinando, H., Seppänen, T., & Alasaarela, E. (2016, October). Comparing features from ECG pattern and HRV analysis for emotion recognition system. In Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016 IEEE Conference on (pp. 1-6). IEEE.


  1. Lim, C. L., Rennie, C., Barry, R. J., Bahramali, H., Lazzaro, I., Manor, B., & Gordon, E. (1997). Decomposing skin conductance into tonic and phasic components. International Journal of Psychophysiology, 25(2), 97-109.
  2. Chaspari, T., Tsiartas, A., Duker, L. I. S., Cermak, S. A., & Narayanan, S. S. (2016, August). EDA-Gram: Designing electrodermal activity fingerprints for visualization and feature extraction. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 403-406). IEEE.
  3. ALADAĞ, S., GÜVEN, A., Özbek, H., & DOLU, N. (2015). Elektrodermal Aktivite Sinyallerinde Gürültü Giderme Yöntemlerinin Karşılaştırılması A Comparison of Denoising Methods for Electrodermal Activity Signals. Vogue, 15(18).
  4. Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of neuroscience methods, 190(1), 80-91.
  5. Fowles, D. C., Christie, M. J., Edelberg, R., Grings, W. W., Lykken, D. T., & Venables, P. H. (1981). Publication recommendations for electrodermal measurements. Psychophysiology, 18(3), 232-239.


  1. Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420-7431.
  2. Reaz, M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1), 11.
  3. Du, S., & Vuskovic, M. (2004, November). Temporal vs. spectral approach to feature extraction from prehensile EMG signals. In Information Reuse and Integration, 2004. IRI 2004. Proceedings of the 2004 IEEE International Conference on (pp. 344-350). IEEE.
  4. Künecke, J., Hildebrandt, A., Recio, G., Sommer, W., & Wilhelm, O. (2014). Facial EMG responses to emotional expressions are related to emotion perception ability. PloS one, 9(1), e84053.

Other resources

  1. GSR Everything you need to know about Galvanic Skin Response to push your insights into emotional behavior, iMotions