<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The problem of human-computer interaction mental workload in flight driving has great reference value for the prevention of safety hazards in aviation driving. This paper analyzes and studies the classification method of mental workload in flight driving by designing different simulated flight experiment tasks. This study uses a combination of EEG signals and subjective evaluation, through the use of convolutional neural networks and long short-term memory network method of combining EEG signals for research and analysis. The accuracy of EEG signal classification is as high as 94.9 %. NASA-TLX evaluation results show that there is a positive correlation between task load difficulty and evaluation score. The results show that the combination of convolutional neural network and long short-term memory network is suitable for pilots ’ mental workload classification. This study has important practical significance for flight accidents caused by pilots ’ mental workload.</p>
</abstract>

The problem of human-computer interaction mental workload in flight driving has great reference value for the prevention of safety hazards in aviation driving. This paper analyzes and studies the classification method of mental workload in flight driving by designing different simulated flight experiment tasks. This study uses a combination of EEG signals and subjective evaluation, through the use of convolutional neural networks and long short-term memory network method of combining EEG signals for research and analysis. The accuracy of EEG signal classification is as high as 94.9 %. NASA-TLX evaluation results show that there is a positive correlation between task load difficulty and evaluation score. The results show that the combination of convolutional neural network and long short-term memory network is suitable for pilots ’ mental workload classification. This study has important practical significance for flight accidents caused by pilots ’ mental workload.

Research on Pilots ’ Mental Workload Classification in Simulated Flight

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Research on Pilots \u2019 Mental Workload Classification in Simulated Flight"}

The problem of human-computer interaction mental workload in flight driving has great reference value for the prevention of safety hazards in aviation driving....

High loads	Low loads	Control group
Indicated airspeed meter	Indicated airspeed meter	—
Pitching scale information	Pitching scale information	—
Altimeter information	Altimeter information	—
Directional angle information	—	—
Rolling corners information	—	—
Status of the steering compartment	—	—
Landing gear conditions	—	—
Engine status	—	—

Name of the layer	Hyperparameters	Input vector	Output vector
EEG_Input	None	(256,1)	(256,1)
Conv1D	Kernel size=5
Conv1D	stride = 2	(256,1)	(128,64)
Conv2D	Kernel size=3
Conv2D	stride = 2	(128,64)	(64,128)
Reshape	None	(64,128)	(8192,1)
Max Pooling	pool size=128	(8192,1)	(64,1)
LSTM	units=32	(64,1)	(32,1)
Dense	units=64	(32,1)	(64,1)
Dense	units=32	(64,1)	(32,1)

Frequency band range(Hz)	Band names
1-4	delta (δ)
4-8	theta (θ)
8-13	alpha (α)
13-30	beta (β)
>30	gamma (γ)

Research on Pilots ’ Mental Workload Classification in Simulated Flight

Data publikacji: 31 maj 2023

Zakres stron: 75 - 82

DOI: https://doi.org/10.2478/ijanmc-2023-0048

Słowa kluczowe
Mental Workload, EEG, Convolutional Neural Network, Long Short-Term Memory Network, Subjective Evaluation Method

© 2023 Jinna Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

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Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

INFORMATION ON THE DIALS TO BE MONITORED FOR DIFFERENT DIFFICULTY TASKS

MODEL TRAINING PARAMETERS

TABLE OF FREQUENCY BANDS OF EEG SIGNALS

Research on Pilots ’ Mental Workload Classification in Simulated Flight

Data publikacji: 31 maj 2023

Zakres stron: 75 - 82

DOI: https://doi.org/10.2478/ijanmc-2023-0048

Słowa kluczoweMental Workload, EEG, Convolutional Neural Network, Long Short-Term Memory Network, Subjective Evaluation Method

© 2023 Jinna Xue et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

INFORMATION ON THE DIALS TO BE MONITORED FOR DIFFERENT DIFFICULTY TASKS

MODEL TRAINING PARAMETERS

TABLE OF FREQUENCY BANDS OF EEG SIGNALS

Słowa kluczowe
Mental Workload, EEG, Convolutional Neural Network, Long Short-Term Memory Network, Subjective Evaluation Method