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The present study attempted to establish an effective discrimination and prediction model that can be applied to evaluate mental workload changes in human-machine interaction processes on aircraft flight deck. By adopting a combined measure based on primary task measurement, subjective measurement and physiological measurement, this study developed both experimental measurement and theoretical modeling of mental workload under flight simulation task conditions. The experimental results showed that, as the mental workload increased, the peak amplitude of Mismatch negativity MMN was significantly increased, SDNN the standard deviation of R-R intervals was significantly decreased,the number of eye blink was decreased significantly. Finally, a comprehensive mental workload discrimination and prediction model for the aircraft flight deck display interface was constructed by the Bayesian Fisher discrimination and classification method. The model's accuracy was checked by original validation method. When comparing the prediction and discrimination results of this comprehensive model with that of single indices, the former showed much higher accuracy.

A Theoretical Model of Mental Workload in Pilots Based on Multiple Experimental Measurements