With respect to the ergonomic evaluation and optimization in the mental task design of the aircraft cockpit display interface, the experimental measurement and theoretical modeling of mental workload were carried out under flight simulation task conditions using the performance evaluation, subjective evaluation and physiological measurement methods. The experimental results show that with an increased mental workload, the detection accuracy of flight operation significantly reduced and the reaction time was significantly prolonged; the standard deviation of R-R intervals (SDNN) significantly decreased, while the mean heart rate exhibited little change; the score of NASA_TLX scale significantly increased. On this basis, the indexes sensitive to mental workload were screened, and an integrated model for the discrimination and prediction of mental workload of aircraft cockpit display interface was established based on the Bayesian Fisher discrimination and classification method. The original validation and cross-validation methods were employed to test the accuracy of the results of discrimination and prediction of the integrated model, and the average prediction accuracies determined by these two methods are both higher than 85%. Meanwhile, the integrated model shows a higher accuracy in discrimination and prediction of mental workload compared with single indexes. The model proposed in this paper exhibits a satisfactory coincidence with the measured data and could accurately reflect the variation characteristics of the mental workload of aircraft cockpit display interface, thus providing a basis for the ergonomic evaluation and optimization design of the aircraft cockpit display interface in the future. ª 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.

Behavioral performance, subjective assessment based on NASA Task Load Index (NASA-TLX), as well as physiological measures indexed by electrocardiograph (ECG), event-related potential (ERP), and eye tracking data were used to assess the mental workload (MW) related to flight tasks. Flight simulation tasks were carried out by 12 healthy participants under different MW conditions. The MW conditions were manipulated by setting the quantity of flight indicators presented on the head-up display (HUD) in the cruise phase. In this experiment, the behavioral performance and NASA-TLX could reflect the changes of MW ideally. For physiological measures, the indices of heart rate variability (HRV), P3a, pupil diameter and eyelid opening were verified to be sensitive to MW changes. Our findings can be applied to the comprehensive evaluation of MW during flight tasks and the further quantitative classification.

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Abstract. BACKGROUND: Human factors involved with visual attention mechanism and fatigue are critical causes of modern aviation accidents. OBJECTIVE: To investigate the connection between attention and flight fatigue, a mathematical model of pilot's visual attention allocation was established based on information processing channels. Multi-task condition and current psychophysical state were taken into account as well. METHODS: Sixteen participants were recruited to perform a long-term dual-task in a Boeing 737-800 flight simulator. The primary task was an envelope flight task and the secondary was an unusual attitude (UA) recovery task. Reaction time of the secondary task was recorded as a behavior performance index, while heart rate and respiration rate were measured as physiological indices as well as fixation distribution as attention allocation index. RESULTS:The experiment results showed a significant affect of experiment time that indicated the occurrence and influence of fatigue. Eye movement tracking also revealed good agreement with the predictable model and hence verified its effectiveness. Moreover, applicability of the model was validated under flight fatigue and multiple tasks condition. CONCLUSION: The current study provided a quantitative connection between pilot's visual attention allocation and flight fatigue, which was verified in the ergonomics experiment.

Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.

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To predict changes of situation awareness (SA) for pilot operating with different display interfaces and tasks, a qualitative analysis and quantitative calculation joint SA model was proposed. Based on the situational awareness model according to the attention allocation built previously, the pilot cognitive process for the situation elements was analyzed according to the ACT-R (Adaptive Control of Thought, Rational) theory, which explained how the SA was produced. To verify the validity of this model, 28 subjects performed an instrument supervision task under different experiment conditions. Situation Awareness Global Assessment Technique (SAGAT), 10-dimensional Situational Awareness Rating Technique (10-D SART), performance measure and eye movement measure were adopted for evaluating SAs under different conditions. Statistical analysis demonstrated that the changing trend of SA calculated by this model was highly correlated with the experimental results. Therefore the situational awareness model can provide a reference for designing new cockpit display interfaces and help reducing human errors.

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