EEG Patterns Analysis in the Process of Recovery from Interruptions

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)


This paper reports the results of the experiment addressing the recovery from interruption phenomenon in terms of brain activity patterns. The aim of the experiment was to find out whether it is possible to find any significant differences in brain activity between subjects performing the task in the recovery period better or worse than the control group. The main outcome from the experiment was that the brain activity of the subjects who performed better than the control group did not change significantly during back to task period compared to interruption period. On the contrary, for subjects whose performance was worse than in the control group, the significant changes in signal power in some frequency bands were found.


EEG pattern analysis Brain activity patterns Interruptions Recovery from interruption Human–computer interaction HCI 


  1. 1.
    Ramchurn, S.D., Deitch, B., Thompson, M.K., De Roure, D.C., Jennings, N.R., Luck, M.: Minimising intrusiveness in pervasive computing environments using multi-agent negotiation. In: Proceedings of the First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous’04). IEEE Computer Society, Los Alamitos, pp. 364–372 (2004)Google Scholar
  2. 2.
    Appelbaum, S.H., Marchionni, A., Fernandez, A.: The multi-tasking paradox: perceptions, problems and strategies. Manag. Decis. 46(9), 1313–1325 (2008)CrossRefGoogle Scholar
  3. 3.
    Altmann, E.M., Trafton, J.G.: Timecourse of recovery from task interruption: data and a model. Psychon. Bull. Rev. 14(6), 1079–1084 (2007)CrossRefGoogle Scholar
  4. 4.
    Hodgetts, H.M., Jones, D.M.: Contextual cues aid recovery from interruption: the role of associative activation. J. Exp. Psychol.: Learn. Mem. Cogn. 32(5), 1120–1132 (2006)Google Scholar
  5. 5.
    Xia, L., Sudharshan, D.: Effects of interruptions on consumer online decision processes. J. Consum. Psychol. 12(3), 265–280 (2002)CrossRefGoogle Scholar
  6. 6.
    Mandel, N., Johnson, E.: Constructing preferences online: can web pages change what you want? (Working paper). University of Pennsylvania, Wharton School, Philadelphia (1999)Google Scholar
  7. 7.
    Corragio, L.: Deleterious effects of intermittent interruptions on the task performance of knowledge workers: a laboratory investigation. Unpublished doctoral dissertation, University of Arizona, Tucson (1990)Google Scholar
  8. 8.
    Zhang, Y., Pigot, I., Mayers, A.: Attention switching during interruptions. In: Proceedings of the Third International Conference on Machine Learning and Cybernetics. Shanghai, 26–29 August 2004, pp. 276–281Google Scholar
  9. 9.
    Cohen, S.: Aftereffects of stress on human performance and social behavior: a review of research and theory. Psychol. Bull. 88(1), 82–108 (1980)CrossRefGoogle Scholar
  10. 10.
    Edwards, M.B., Gronlund, S.D.: Task Interruption and its effects on memory. Memory 6(6), 665–687 (1998)CrossRefGoogle Scholar
  11. 11.
    Baron, R.S.: Distraction-conflict theory: progress and problems. Adv. Exp. Soc. Psychol. 19, 1–39 (1986)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Speier, C., Valacich, J.S., Vessey, I.: The influence of task interruption on individual decision making: an information overload perspective. Decis. Mak. 30(2), 337–360 (1999)Google Scholar
  13. 13.
    Speier, Ch.: The effect of task interruption and information presentation on individual decision making. Unpublished doctoral dissertation, Indiana University, Bloomington (1996)Google Scholar
  14. 14.
    Zha, W., Wu, H.D.: The impact of online disruptive ads on users’ comprehension, evaluation of site credibility, and sentiment of intrusiveness. Am. Commun. J. 16(2), 15–28 (2014)Google Scholar
  15. 15.
    Van Bergen, A.: Task Interruption. North-Holland, Amsterdam (1968)Google Scholar
  16. 16.
    Jasper, H.H.: The ten-twenty electrode system of the international federation in electroencephalography and clinical neurophysiology. EEG J. 10, 371–375 (1958)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

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