Classification Algorithms in Adaptive Systems for Neuro-Ergonomic Applications

  • Grace TeoEmail author
  • Lauren Reinerman-Jones
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)


Adaptive systems typically comprise components that interrelate and interact to enable the whole system to respond and adjust to changes in the environment, operator, and task in order to regulate or maintain a level of performance or homeostasis. In so doing, they enable a degree of individualization and customization for many technological innovations such as managing the use of automation. Adaptive systems often involve some kind of feedback or closed-loop which requires a criteria for determining invoking thresholds, as well as some type of classification algorithm that models the type of changes to which the system has to adapt. This paper outlines the issues, considerations, and challenges associated with classification in adaptive systems, and reviews several algorithms that implement the feedback loop in neuro-ergonomic applications. These include logistic regression, Naïve-Bayes, artificial neural networks (ANN), and support vector machines (SVM) techniques.


Classification Adaptive systems Neuro-ergonomics 



This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911 NF-14-2-0021. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied of the Army Research Laboratory of or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.


  1. 1.
    Baldwin, C.L., Penaranda, B.N.: Adaptive training using an artificial neural network and EEG metrics for within- and cross-task workload classification. NeuroImage 59, 48–56 (2012). Scholar
  2. 2.
    Hannula, M., Huttunen, K., Koskelo, J., Laitinen, T., Leino, T.: Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator. Comput. Biol. Med. 38, 1163–1170 (2008). Scholar
  3. 3.
    Prinzel III, L.J., Freeman, F.G., Scerbo, M.W., Mikulka, P.J., Pope, A.T.: Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component. Hum. Factors 45, 601–614 (2003)CrossRefGoogle Scholar
  4. 4.
    Prinzel, L.J., Freeman, F.G., Scerbo, M.W., Mikulka, P.J., Pope, A.T.: A closed-loop system for examining psychophysiological measures for adaptive task allocation. Int. J. Aviat. Psychol. 10, 393–410 (2000). Scholar
  5. 5.
    Freeman, F.G., Mikulka, P.J., Scerbo, M.W., Prinzel, L.J., Clouatre, K.: Evaluation of a psychophysiologically controlled adaptive automation system, using performance on a tracking task. Appl. Psychophysiol. Biofeedback 25, 103–115 (2000)CrossRefGoogle Scholar
  6. 6.
    Wilson, G.F., Russell, C.A.: Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Hum. Factors Soc. J. Hum. Factors Ergon. 49, 1005–1018 (2007). Scholar
  7. 7.
    Wilson, G.F., Russell, C.A.: Real-time assessment of mental workload using psychophysiological measures and artificial neural networks. Hum. Factors 45, 635–644 (2003)CrossRefGoogle Scholar
  8. 8.
    Christensen, J.C., Estepp, J.R., Wilson, G.F., Russell, C.A.: The effects of day-to-day variability of physiological data on operator functional state classification. NeuroImage 59, 57–63 (2012). Scholar
  9. 9.
    Feigh, K.M., Dorneich, M.C., Hayes, C.C.: Toward a characterization of adaptive systems: a framework for researchers and system designers. Hum. Factors J. Hum. Factors Ergon. Soc. 54, 1008–1024 (2012). Scholar
  10. 10.
    Hockey, G.R.J.: Operator Functional State: The Assessment and Prediction of Human Performance Degradation in Complex Tasks. IOS Press, Amsterdam (2003)Google Scholar
  11. 11.
    Parasuraman, R., Mouloua, M., Molloy, R.: Effects of adaptive task allocation on monitoring of automated systems. Hum. Factors 38, 665–679 (1996)CrossRefGoogle Scholar
  12. 12.
    Dimensionality Reduction Algorithms: Strengths and Weaknesses.
  13. 13.
    Field, A.: Discovering statistics using SPSS. Sage publications, Thousand Oaks (2009)zbMATHGoogle Scholar
  14. 14.
  15. 15.
    Mehrotra, K., Mohan, C.K., Ranka, S.: Elements of Artificial Neural Networks (Complex Adaptive Systems). MIT Press, Cambridge, MA (1997)Google Scholar
  16. 16.
    Ruck, D.W., Rogers, S.K., Kabrisky, M.: Feature selection using a multilayer perceptron. J. Neural Netw. Comput. 2, 40–48 (1990)Google Scholar
  17. 17.
    Provost, F., Fawcett, T.: Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media Inc, Sebastopol (2013)Google Scholar
  18. 18.
    Besson, P., Dousset, E., Bourdin, C., Bringoux, L., Marqueste, T., Mestre, D.R., Vercher, J.-L.: Bayesian network classifiers inferring workload from physiological features: compared performance. In: 2012 IEEE Intelligent Vehicles Symposium (IV), pp. 282–287. IEEE (2012)Google Scholar
  19. 19.
    Johannes, B., Gaillard, A.W.K.: A methodology to compensate for individual differences in psychophysiological assessment. Biol. Psychol. 96, 77–85 (2014). Scholar
  20. 20.
    Wang, Z., Hope, R.M., Wang, Z., Ji, Q., Gray, W.D.: Cross-subject workload classification with a hierarchical Bayes model. NeuroImage 59, 64–69 (2012). Scholar
  21. 21.
    Understanding Support Vector Machine algorithm from examples (along with code).
  22. 22.
    Yeo, M.V.M., Li, X., Shen, K., Wilder-Smith, E.P.V.: Can SVM be used for automatic EEG detection of drowsiness during car driving? Saf. Sci. 47, 115–124 (2009). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Simulation and Training, University of Central FloridaOrlandoUSA

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