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Mental workload and task engagement evaluation based on changes in electroencephalogram

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Abstract

Purpose

In this study, electroencephalogram (EEG) signals were used to assess subject’s mental workload and task engagement level during a set of neurocognitive tasks in an experimental space suit.

Methods

EEG signals were collected using a wireless EEG system during two experimental conditions — when subjects did/did not wear space suit. Brain state changes based on EEG changes were quantified and compared with the direct responses of the subjects for different tasks. In addition, a statistical test of significance on the computed EEG index for the two experimental conditions was performed.

Results

It was found that the spacesuit experiment introduced a greater mental workload where subject’s stress levels were higher than control experiment. Results indicated significant differences in task engagement between the spacesuit and control experiments for most of the tasks.

Conclusions

The findings could be useful in monitoring astronaut’s or human subject’s cognitive performance in assuring safety as well as improving the performance.

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References

  1. Moore-Ede M. The twenty-four-hour society: Understanding human limits in a world that never stops. Addison Wesley; 1994.

  2. Molloy R, Singh IL, Parasuram R. Performance consequences of automation induced “complacency”. Int J Aviats Psychol. 1993; 3:1–23.

    Article  Google Scholar 

  3. Pope AT, Bogart EH, Bartolome DS. Biocybernetic system evaluates indices of operator engagement in atuomated task. J Biol Psychol. 1995; 40:187–195.

    Article  Google Scholar 

  4. Prinzel LJ, Freeman FG, Scerbo MW, Mikulka PJ, Pope AT. Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component. Hum Factors. 2003; 45(4):601–613.

    Article  Google Scholar 

  5. Levendowski DJ, Ramsey CK, Berka C. Evaluation of an EEGworkload model in Aegis simulation environment. Proc Int Soc Opt Eng. 2005; 90–99.

  6. Inlow M, Makeig S. Lapses in alertness: Coherence of fluctuations in performances and EEG spectrum. Electroen Clin Neuro. 1993; 86:23–35.

    Google Scholar 

  7. Jung TP, Makeig S. Changes in alertness are principal component in variance in the EEG spectrum. Neuroreport. 1995; 7:213–216.

    Google Scholar 

  8. Jung TP, Makeig S. Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness. Cognitive Brain Res. 1996; 4:15–25.

    Article  Google Scholar 

  9. Levendowski D, Cvetinovic M, Berka C. Real-time analysis of EEG indices of alertness, cognition, and memory with a wireless headset. Int J Hum-Comput Int. 2004; 17:151–170.

    Google Scholar 

  10. Levendowski DJ, Lumicao MN, Yau A, Davis G, Zivkovic VT, Olmstead RE, Tremoulet PD, Craven PL, Berka C. EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat Space Envir Md. 2007; 78:B231–B244.

    Google Scholar 

  11. Makeig S, Stensmo M, Sejnowski TJ, Tzyy-Ping J. Estimating alertness from EEG power spectrum. IEEE T Bio-med Eng. 1997; 44:60–69.

    Article  Google Scholar 

  12. Wu RC, Jung TP, Liang SF, Huang TY, Lin CT. Estimating driving performance based on EEG spectrum analysis. EURASIP J Appl Signal Process. 2005; 19:3165–3174.

    Google Scholar 

  13. Freeman FG, Scerbo MW, Prinzel LJ. A closed-loop system for examining psychophysiological measures for adaptive task allocation. Int J Aviats Psychol. 2000; 10:393–410.

    Article  Google Scholar 

  14. EEG Technology Review. 2007. http://www.b-alert.com/resources/EEG_Technology_Review_JAN_2007.pdf.

  15. Baker SN. Oscillatory interactions between sensorimotor cortex and the periphery. Curr Opin Neurobiol. 2007; doi: 10.1016/j.conb.2008.01.007

  16. Klimesch W, Sauseng P, Hanslmayr S. Alpha phase reset contributes to the generation of ERPs. Cereb Cortex. 2007; 17(1):1–8.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Biomedical Signal Processing Laboratory, Department of Electrical Engineering, University of North Dakota, Grand Forks, ND, 58202, USA

    Ahmed Fazle Rabbi, Abongwa Zony & Reza Fazel-Rezai

  2. Department of Space Studies, University of North Dakota, Grand Forks, ND, 58202, USA

    Pablo de Leon

Authors
  1. Ahmed Fazle Rabbi
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  2. Abongwa Zony
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  3. Pablo de Leon
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  4. Reza Fazel-Rezai
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Corresponding author

Correspondence to Reza Fazel-Rezai.

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Rabbi, A.F., Zony, A., de Leon, P. et al. Mental workload and task engagement evaluation based on changes in electroencephalogram. Biomed. Eng. Lett. 2, 139–146 (2012). https://doi.org/10.1007/s13534-012-0065-8

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  • DOI: https://doi.org/10.1007/s13534-012-0065-8

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