ArticleCognitive neurodynamics2013
Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method.
Article in Cognitive neurodynamics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Instantaneous mental workload assessment using time-frequency analysis and semi-supervised learning.Cognitive neurodynamics · 2020Article
- Regularized common spatial patterns with subject-to-subject transfer of EEG signals.Cognitive neurodynamics · 2017Article
- Survey on granularity clustering.Cognitive neurodynamics · 2015Review
- Localization of neural efficiency of the mathematically gifted brain through a feature subset selection method.Cognitive neurodynamics · 2015Article
- Article
- A novel approach for pilot error detection using Dynamic Bayesian Networks.Cognitive neurodynamics · 2014Article
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Authors and funding
5 authors.
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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.
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