Biomedical Engineering Program, University of North Dakota, Grand Forks, ND, USA.
Departments of Aviation, University of North Dakota, Grand Forks, ND, USA.
Sci Rep. 2023 Feb 13;13(1):2507. doi: 10.1038/s41598-023-29647-0.
Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.
即使在正常的飞行操作中，飞机的飞行员也面临着不同程度的认知工作量。低认知工作量的时期可能会被高认知工作量的时期所取代，反之亦然。在这种需求不断变化的情况下，由于飞行员感到无聊或认知任务需求过高，可能会增加出错的可能性。为了进一步了解航空中的认知工作量，本研究从十名（10）大学生航空学员在单引擎飞机的实时飞行环境中收集了脑电图（EEG）数据。每位飞行员都拥有联邦航空管理局（FAA）的商业飞行员证书，以及 FAA 一级或二级医疗证书。每位飞行员都按照标准的飞行剖面飞行，代表平均的仪表飞行训练序列。对于数据分析，我们使用了记录的 EEG 信号的四个主要子带：delta、theta、alpha 和 beta。计算了每个子带在 20 个电极上的功率谱密度（PSD）和对数能量熵，并对其进行了两种特征选择算法（递归特征消除（RFE）和套索交叉验证（LassoCV），以及由支持向量机、随机森林和逻辑回归组成的堆叠集成机器学习算法。此外，还使用超参数优化和十折交叉验证来提高模型的性能、可靠性和泛化能力。特征选择步骤得到了 15 个特征，可以作为飞行员认知工作量状态的指标。然后，将这些特征应用于堆叠集成算法，使用 RFE 算法选择的特征可获得最高的结果，准确率为 91.67%（±0.11），精度为 93.89%（±0.09），召回率为 91.67%（±0.11），F1 得分为 91.22%（±0.12），平均 ROC-AUC 为 0.93（±0.06）。研究结果表明，PSD 和对数能量熵的结合以及精心设计的机器学习算法表明，使用 EEG 来区分低、中、高工作量阶段的潜力，以增强飞机系统设计，包括飞行自动化功能，以提高航空安全性。
