Abstract
To explore mental workload and methods for dynamically monitoring mental workload imposed by complex tasks, this study constructed a virtual operating environment according to three cognitive steps: perception, judgment-making and action execution. Dynamic characteristics of mental workload were then analyzed employing subjective questionnaires, performance data and electroencephalography (EEG) characteristics. The analysis of non-linear dynamic characteristics of EEG signals showed that the fractal box dimension features of EEG signals are quite sensitive to the level of mental workload, exercising a significant impact on the four brain areas. The sample entropy is also quite sensitive to the level of mental workload, exercising a significant impact on the frontal, central and occipital areas. Based on this study, operational tasks can be dynamically assigned according to the state of personnel load and the safety and efficiency of the operation of the human–machine system can be ensured.
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