Advertisement

Application of EEG Metrics in the Decision-Making Process

  • Mateusz PiwowarskiEmail author
  • Uma Shankar Singh
  • Kesra Nermend
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

The decision-making process is a complex task uses the multi-criteria methods in the formalized decision support. Decisions are direct reflection of decision maker preferences. Multi-criteria methods use different methodological approaches (algorithms) to determine the final assessment of decision variants (e.g., ranking). Decision maker must do many actions (partial evaluations) in some of these methods. Issues of the decision maker’s engagement in the assessment process arise which can be identified using measurements by EEG. It is possible to identify various internal processes occurring with the decision maker during individual stages of the calculation procedure. Various types of EEG metrics are used for this, such as the index of frontal asymmetry, engagement, distraction, etc.

Keywords

EEG metrics Decision making Multi-criteria methods 

Notes

Acknowledgements

The project financed within the Regional Excellence Initiative programme of the Minister of Science and Higher Education of Poland, years 2019-2022, project no. 001/RID/2018/19, financing 10 684 000,00 PLN.

References

  1. 1.
    Grünig, R., Kühn, R.: Successful Decision-Making. A Systematic Approach to Complex Problems, 3rd edn. Springer, Berlin (2013)Google Scholar
  2. 2.
    Pei, Z.: Rational decision making models with incomplete weight information for production line assessment. Inf. Sci. 222, 696–716 (2013).  https://doi.org/10.1016/j.ins.2012.07.060CrossRefGoogle Scholar
  3. 3.
    Aronson, E., Wilson, T.D., Akert, R.M.: Social Psychology, 9th edn. Pearson, Boston (2016)Google Scholar
  4. 4.
    Kahneman, D.: Thinking, Fast and Slow. Penguin Books, London (2012)Google Scholar
  5. 5.
    Wang, Y.: The Theoretical Framework of Cognitive Informatics. Int. J. Cognitive Inform. Nat. Intell. 1, 1–27 (2007).  https://doi.org/10.4018/jcini.2007010101CrossRefGoogle Scholar
  6. 6.
    von Winterfeldt, D., Edwards, W.: Decision Analysis and Behavioral Research. Cambridge University Press, Cambridge (1986)Google Scholar
  7. 7.
    Roy, B.: Multicriteria Methodology for Decision Aiding. Springer, US (1996)CrossRefGoogle Scholar
  8. 8.
    Vincke, P.: Multicriteria Decision-Aid. J. Wiley, New York (1992)Google Scholar
  9. 9.
    International Society on MCDM.: Multiple criteria decision making. http://www.mcdmsociety.org (2015). Accessed 22 Apr 2016
  10. 10.
    Mota, P., Campos, A.R., Neves-Silva, R.: First look at MCDM: Choosing a decision method. Adv. Smart Syst. Res. 3, 25–30 (2013)Google Scholar
  11. 11.
    Figueira, J., Greco, S., Ehrgott, M.: Multiple Criteria Decision Analysis State of the Art Surveys. Springer, New York (2005)CrossRefGoogle Scholar
  12. 12.
    Huang, I.B., Keisler, J., Linkov, I.: Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Sci. Total Environ. 409, 3578–3594 (2011).  https://doi.org/10.1016/j.scitotenv.2011.06.022CrossRefGoogle Scholar
  13. 13.
    Hwang, C.-L., Yoon, K.: Multiple Attribute Decision Making. Springer, Berlin, Heidelberg (1981)CrossRefGoogle Scholar
  14. 14.
    Karczmarczyk, A., Jankowski, J., Wątróbski, J.: Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks. PLoS ONE 13, e0209372 (2018).  https://doi.org/10.1371/journal.pone.0209372CrossRefGoogle Scholar
  15. 15.
    Nermend, K.: Metody analizy wielokryterialnej i wielowymiarowej we wspomaganiu decyzji (in Polish). Wydawnictwo Naukowe PWN, Warszawa (2017)Google Scholar
  16. 16.
    Piwowarski, M., Miłaszewicz, D., Łatuszyńska, M., et al.: Application of the vector measure construction method and technique for order preference by similarity ideal solution for the analysis of the dynamics of changes in the poverty levels in the European union countries. Sustainability 10, 2858 (2018).  https://doi.org/10.3390/su10082858CrossRefGoogle Scholar
  17. 17.
    Roy, B.: Multicriteria Methodology for Decision Aiding. Springer, New York, NY (2013)Google Scholar
  18. 18.
    Wątróbski, J., Jankowski, J., Ziemba, P., et al.: Generalised framework for multi-criteria method selection. Omega 86, 107–124 (2019).  https://doi.org/10.1016/j.omega.2018.07.004CrossRefGoogle Scholar
  19. 19.
    Zanakis, S.H., Solomon, A., Wishart, N., Dublish, S.: Multi-attribute decision making: A simulation comparison of select methods. Eur. J. Oper. Res. 107, 507–529 (1998).  https://doi.org/10.1016/S0377-2217(97)00147-1CrossRefGoogle Scholar
  20. 20.
    Saaty, T.L.: Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process. RWS Publications, Pittsburg (2000)Google Scholar
  21. 21.
    Saaty, T.L.: Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburg (1996)Google Scholar
  22. 22.
    Cypryjański, J., Grzesiuk, A.: Expressing our preferences with the use of AHP: The game is not worth the candle? In: Nermend, K., Łatuszyńska, M. (eds.) Problems, Methods and Tools in Experimental and Behavioral Economics, pp. 155–165. Springer, Cham (2018)CrossRefGoogle Scholar
  23. 23.
    Ziemba, P., Wątróbski, J., Jankowski,J., Piwowarski, M.: Research on the properties of the AHP in the environment of inaccurate expert evaluations. In: Nermend, K., Łatuszyńska, M (eds) Selected Issues in Experimental Economics: Proceedings of the 2015 Computational Methods in Experimental Economics (CMEE) Conference. Springer International Publishing, Cham, pp 227–243 (2016)CrossRefGoogle Scholar
  24. 24.
    Nermend, K., Piwowarski, M.: Cognitive neuroscience techniques in supporting decision making and the analysis of social campaign, pp. 1–13. Ishik Univeristy, Erbil, Iraq (2018)Google Scholar
  25. 25.
    Bear, M.F., Connors, B.W., Paradiso, M.A.: Neuroscience: Exploring the Brain, 3rd edn. Lippincott Williams & Wilkins, Baltimore, Md (2007)Google Scholar
  26. 26.
    Nunez, P, Srinavasan, R.: Electric fields of the brain (2006)Google Scholar
  27. 27.
    Malmivuo, J., Plonsey, R.: Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford Univ. Press, New York, NY (1995)CrossRefGoogle Scholar
  28. 28.
    Sharbrough, F., Chatrian, G.E., Lesser, R., et al.: American Electroencephalographic Society guidelines for standard electrode position nomenclature (1991)Google Scholar
  29. 29.
    Ahern, G.L., Schwartz, G.E.: Differential lateralization for positive and negative emotion in the human brain: EEG spectral analysis. Neuropsychologia 23, 745–755 (1985).  https://doi.org/10.1016/0028-3932(85)90081-8CrossRefGoogle Scholar
  30. 30.
    Coan, J.A., Allen, J.J.B.: Frontal EEG asymmetry as a moderator and mediator of emotion. Biol. Psychol. 67, 7–50 (2004).  https://doi.org/10.1016/j.biopsycho.2004.03.002CrossRefGoogle Scholar
  31. 31.
    Harmon-Jones, E., Gable, P.A., Peterson, C.K.: The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update. Biol. Psychol. 84, 451–462 (2010).  https://doi.org/10.1016/j.biopsycho.2009.08.010CrossRefGoogle Scholar
  32. 32.
    Smith, E.E., Reznik, S.J., Stewart, J.L., Allen, J.J.B.: Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. Int. J. Psychophysiol. 111, 98–114 (2017).  https://doi.org/10.1016/j.ijpsycho.2016.11.005CrossRefGoogle Scholar
  33. 33.
    Stewart, J.L., Bismark, A.W., Towers, D.N., et al.: Resting frontal EEG asymmetry as an endophenotype for depression risk: Sex-specific patterns of frontal brain asymmetry. J. Abnorm. Psychol. 119, 502–512 (2010).  https://doi.org/10.1037/a0019196CrossRefGoogle Scholar
  34. 34.
    Nusslock, R., Shackman, A.J., Harmon-Jones, E., et al.: Cognitive vulnerability and frontal brain asymmetry: Common predictors of first prospective depressive episode. J. Abnorm. Psychol. 120, 497–503 (2011).  https://doi.org/10.1037/a0022940CrossRefGoogle Scholar
  35. 35.
    Blackhart, G.C., Kline, J.P., Donohue, K.F., et al.: Affective responses to EEG preparation and their link to resting anterior EEG asymmetry. Pers. Individ. Differ. 32, 167–174 (2002).  https://doi.org/10.1016/S0191-8869(01)00015-0CrossRefGoogle Scholar
  36. 36.
    Towers, D.N., Allen, J.J.B.: A better estimate of the internal consistency reliability of frontal EEG asymmetry scores. Psychophysiology 46, 132–142 (2009).  https://doi.org/10.1111/j.1469-8986.2008.00759.xCrossRefGoogle Scholar
  37. 37.
    Cook, I.A., O’Hara, R., Uijtdehaage, S.H.J., et al.: Assessing the accuracy of topographic EEG mapping for determining local brain function. Electroencephalogr. Clin. Neurophysiol. 107, 408–414 (1998).  https://doi.org/10.1016/S0013-4694(98)00092-3CrossRefGoogle Scholar
  38. 38.
    Davidson, R.J., Chapman, J.P., Chapman, L.J., Henriques, J.B.: Asymmetrical brain electrical activity discriminates between psychometrically-matched verbal and spatial cognitive tasks. Psychophysiology 27, 528–543 (1990).  https://doi.org/10.1111/j.1469-8986.1990.tb01970.xCrossRefGoogle Scholar
  39. 39.
    Tops, M., Boksem, M.A.S.: Absorbed in the task: Personality measures predict engagement during task performance as tracked by error negativity and asymmetrical frontal activity. Cognitive Affect. Behav. Neurosci. 10, 441–453 (2010).  https://doi.org/10.3758/CABN.10.4.441CrossRefGoogle Scholar
  40. 40.
    Pintrich, P.R., de Groot, E.V.: Motivational and self-regulated learning components of classroom academic performance. J. Educ. Psychol. 82, 33–40 (1990).  https://doi.org/10.1037/0022-0663.82.1.33CrossRefGoogle Scholar
  41. 41.
    Csikszentmihalyi, M.: Flow: The psychology of optimal experience. Nachdr, Harper [and] Row, New York (2009)Google Scholar
  42. 42.
    O’Brien, H.L., Toms, E.G.: What is user engagement? A conceptual framework for defining user engagement with technology. J. Am. Soc. Inform. Sci. Technol. 59, 938–955 (2008).  https://doi.org/10.1002/asi.20801CrossRefGoogle Scholar
  43. 43.
    Mcmahan, T., Parberry, I., Parsons, T.: Evaluating Electroencephalography Engagement Indices during Video Game Play (2015)Google Scholar
  44. 44.
    Berka, C., Levendowski, D.J., Lumicao, M.N., et al.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(Supplement 1), B231–B244(14) (2007a)Google Scholar
  45. 45.
    Freeman, F.G., Mikulka, P.J., Prinzel, L.J., Scerbo, M.W.: Evaluation of an adaptive automation system using three EEG indices with a visual tracking task. Biol. Psychol. 50, 61–76 (1999).  https://doi.org/10.1016/S0301-0511(99)00002-2CrossRefGoogle Scholar
  46. 46.
    Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biol. Psychol. 40, 187–195 (1995).  https://doi.org/10.1016/0301-0511(95)05116-3CrossRefGoogle Scholar
  47. 47.
    Gevins, A.: High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex 7, 374–385 (1997).  https://doi.org/10.1093/cercor/7.4.374CrossRefGoogle Scholar
  48. 48.
    Smith, M.E., Gevins, A.: Neurophysiologic monitoring of mental workload and fatigue during operation of a flight simulator. In: Caldwell, J.A., Wesensten, N.J. (eds.) Orlando, p. 116. Florida, USA (2005)Google Scholar
  49. 49.
    Yamada, F.: Frontal midline theta rhythm and eyeblinking activity during a VDT task and a video game: useful tools for psychophysiology in ergonomics. Ergonomics 41, 678–688 (1998).  https://doi.org/10.1080/001401398186847CrossRefGoogle Scholar
  50. 50.
    Lin, C-T., Chen, S-A., Ko, L-W., Wang, Y-K.: EEG-based brain dynamics of driving distraction. In: The 2011 International Joint Conference on Neural Networks. IEEE, San Jose, CA, USA, pp. 1497–1500 (2011)Google Scholar
  51. 51.
    Almahasneh, H., Chooi, W.-T., Kamel, N., Malik, A.S.: Deep in thought while driving: An EEG study on drivers’ cognitive distraction. Transp. Res. Part F Traffic Psychol. Behav. 26, 218–226 (2014).  https://doi.org/10.1016/j.trf.2014.08.001CrossRefGoogle Scholar
  52. 52.
    Bajwa, G., Fazeen, M., Dantu, R.: Detecting driver distraction using stimuli-response EEG analysis. (2019). arXiv:190409100 [cs]
  53. 53.
    Berka, C., Levendowski, D.J., Lumicao, M.N., et al.: EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat. Space Environ. Med. 78(Supplement 1), B231–B244(14) (2007b)Google Scholar
  54. 54.
    Borawski, M.: Use of computer game as an element of social campaign focusing attention on reliability of information in the internet. In: Nermend, K., Łatuszyńska, M. (eds.) Problems, Methods and Tools in Experimental and Behavioral Economics, pp. 127–139. Springer, Cham (2018)CrossRefGoogle Scholar
  55. 55.
    Summerfield, C., Mangels, J.A.: Coherent theta-band EEG activity predicts item-context binding during encoding. NeuroImage 24, 692–703 (2005).  https://doi.org/10.1016/j.neuroimage.2004.09.012CrossRefGoogle Scholar
  56. 56.
    Werkle-Bergner, M., Müller, V., Li, S.-C., Lindenberger, U.: Cortical EEG correlates of successful memory encoding: Implications for lifespan comparisons. Neurosci. Biobehav. Rev. 30, 839–854 (2006).  https://doi.org/10.1016/j.neubiorev.2006.06.009CrossRefGoogle Scholar
  57. 57.
    Davidson, R.J.: What does the prefrontal cortex “do” in affect: perspectives on frontal EEG asymmetry research. Biol. Psychol. 67, 219–234 (2004).  https://doi.org/10.1016/j.biopsycho.2004.03.008CrossRefGoogle Scholar
  58. 58.
    Astolfi, L., De Vico, Fallani F., Cincotti, F., et al.: Neural basis for brain responses to TV commercials: A High-resolution EEG study. IEEE Trans. Neural Syst. Rehabil. Eng. 16, 522–531 (2008).  https://doi.org/10.1109/TNSRE.2008.2009784CrossRefGoogle Scholar
  59. 59.
    Mauss, I.B., Robinson, M.D.: Measures of emotion: A review. Cogn. Emot. 23, 209–237 (2009).  https://doi.org/10.1080/02699930802204677CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mateusz Piwowarski
    • 1
    Email author
  • Uma Shankar Singh
    • 2
  • Kesra Nermend
    • 1
  1. 1.Department of Computational Methods in Experimental Economics, Faculty of Economics and Management, Institute of IT in ManagementUniversity of SzczecinSzczecinPoland
  2. 2.Faculty of Administrative Sciences and EconomicsTishk International UniversityErbil-KurdistanIraq

Personalised recommendations