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Zhang JH, Peng XD, Liu H, et al. Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method. Cogn Neurodyn. 2013;7(6):477-94doi: 10.1007/s11571-013-9243-3.
Zhang, J. H., Peng, X. D., Liu, H., Raisch, J., & Wang, R. B. (2013). Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method. Cognitive neurodynamics, 7(6), 477-94. https://doi.org/10.1007/s11571-013-9243-3
Zhang, Jian-Hua, et al. "Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method." Cognitive neurodynamics vol. 7,6 (2013): 477-94. doi: https://doi.org/10.1007/s11571-013-9243-3
Zhang JH, Peng XD, Liu H, Raisch J, Wang RB. Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method. Cogn Neurodyn. 2013 Dec;7(6):477-94. doi: 10.1007/s11571-013-9243-3. Epub 2013 Jan 23. PMID: 24427221; PMCID: PMC3825145.
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Cogn Neurodyn. 2013 Dec;7(6):477-94. doi: 10.1007/s11571-013-9243-3. Epub 2013 Jan 23.

Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.

Cognitive neurodynamics

Jian-Hua Zhang, Xiao-Di Peng, Hua Liu, Jörg Raisch, Ru-Bin Wang

Affiliations

  1. Department of Automation, East China University of Science and Technology, Shanghai, 200237 China ; Institute of Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237 China.
  2. Department of Automation, East China University of Science and Technology, Shanghai, 200237 China.
  3. Control Systems Group, Technical University Berlin, 10587 Berlin, Germany ; Systems and Control Theory Group, Max Planck Institute for Dynamics of Complex Technical Systems, 39106 Magdeburg, Germany.
  4. Institute of Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237 China.

PMID: 24427221 PMCID: PMC3825145 DOI: 10.1007/s11571-013-9243-3

Abstract

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

Keywords: Feature extraction; Fuzzy c-means algorithm; Operator functional state; Pattern classification; Psychophysiological measures

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