Monitoring Mental States of the Human Brain in Action: From Cognitive Test to Real-World Simulations

  • Deepika Dasari
  • Guofa Shou
  • Lei DingEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9183)


Functional mental state of operators in real-world workspaces is a crucial factor in many cognitively demanding tasks. In this paper, we present our recent efforts in studying electroencephalograph (EEG) biomarkers to be used to assess cognitive states of operators. We studied these biomarkers from a simple cognitive task to low- and high-fidelity simulated air traffic control (ATC) tasks, with both novices and professional ATC operators. EEG data were recorded from 25 subjects (in three studies) who performed one of three different cognitively demanding tasks up to 120 min. Our results identified two EEG components with similar spatial and spectral patterns at the group level across all three studies, which both indicated the time-on-task effects in their temporal dynamics. With further developments in the future, the technology and identified biomarkers can be used for real-time monitoring of operators’ cognitive functions in critical task environments and may even provide aids when necessary.


Functional brain imaging EEG Independent component analysis Mental state Human factors 



This work was supported in part by NSF CAREER ECCS-0955260 and DOT-FAA 10-G-008.


  1. 1.
    Boksem, M.A., Meijman, T.F., Lorist, M.M.: Effects of mental fatigue on attention: an ERP study. Cogn. Brain. Res. 25(1), 107–116 (2005)CrossRefGoogle Scholar
  2. 2.
    Cao, A., et al.: NASA TLX: Software for assessing subjective mental workload. Behav. Res. Methods 41(1), 113–117 (2009)CrossRefGoogle Scholar
  3. 3.
    Wilson, G.F., Russell, C.A.: Operator functional state classification using multiple psychophysiological features in an air traffic control task. Hum. Factors: J. Hum. Factors Ergon. Soc. 45(3), 381–389 (2003)CrossRefGoogle Scholar
  4. 4.
    Lorist, M.M., et al.: The influence of mental fatigue and motivation on neural network dynamics; an EEG coherence study. Brain Res. 1270, 95–106 (2009)CrossRefGoogle Scholar
  5. 5.
    Gargiulo, G., et al.: A mobile EEG system with dry electrodes. In: IEEE Biomedical Circuits and Systems Conference BioCAS 2008, IEEE (2008)Google Scholar
  6. 6.
    Shou, G., Ding, L., Dasari, D.: Probing neural activations from continuous EEG in a real-world task: time-frequency independent component analysis. J. Neurosci. Methods 209(1), 22–34 (2012)CrossRefGoogle Scholar
  7. 7.
    Mager, R., et al.: Mismatch and conflict: neurophysiological and behavioral evidence for conflict priming. J. Cogn. Neurosci. 21(11), 2185–2194 (2009)CrossRefGoogle Scholar
  8. 8.
    Shou, G., Ding, L.: Ongoing EEG oscillatory dynamics suggesting evolution of mental fatigue in a color-word matching stroop task. In: 2013 6th International Conference on Neural Engineering (NER), IEEE (2013)Google Scholar
  9. 9.
    Bailey, L.L., et al.: Controller teamwork evaluation and assessment methodology: A scenario calibration study, DTIC Document (1999)Google Scholar
  10. 10.
    Dasari, D., Shou, G., Ding, L.: Investigation of independent components based EEG metrics for mental fatigue in simulated ATC task. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE (2013)Google Scholar
  11. 11.
    Willems, B., Hah, S., Schulz, K.: En route data communications: Experimental human factors evaluation (2010)Google Scholar
  12. 12.
    Kovacevic, N., McIntosh, A.R.: Groupwise independent component decomposition of EEG data and partial least square analysis. Neuroimage 35(3), 1103–1112 (2007)CrossRefGoogle Scholar
  13. 13.
    Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2), 169–195 (1999)CrossRefGoogle Scholar
  14. 14.
    Smith, M.E., et al.: Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction. Hum. Factors: J. Hum. Factors Ergon. Soc. 43(3), 366–380 (2001)CrossRefGoogle Scholar
  15. 15.
    Lal, S.K., Craig, A.: A critical review of the psychophysiology of driver fatigue. Biol. Psychol. 55(3), 173–194 (2001)CrossRefGoogle Scholar
  16. 16.
    Dasari, D., Shou, G., Ding, L.: EEG index for time-on-task mental fatigue in real air traffic controllers obtained via independent component analysis. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (2013)Google Scholar
  17. 17.
    Shou, G., Ding, L.: Detection of EEG spatial–spectral–temporal signatures of errors: a comparative study of ICA-based and channel-based methods. Brain topography, pp. 1–15 (2014)Google Scholar
  18. 18.
    Shou, G., Ding, L.: Frontal theta EEG dynamics in a real-world air traffic control task. in Engineering In: 2013 35th Annual International Conference of the IEEE Medicine and Biology Society (EMBC), IEEE (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Electrical and Computer EngineeringUniversity of OklahomaNormanUSA
  2. 2.Center of Biomedical EngineeringUniversity of OklahomaNormanUSA

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