Liu Chenglin, Zhang Chenyang, Sun Luohao, Liu Kun, Liu Haiyue, Zhu Wenbing, Jiang Chaozhe
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
Entropy (Basel). 2023 Jul 10;25(7):1035. doi: 10.3390/e25071035.
Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots' real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system's performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.
飞行员所经历的高心理负荷（MWL）可能导致反应时间增加或出现错误操作，从而可能危及飞行安全。本研究旨在开发一个功能系统，以协助管理人员识别和检测飞行员的实时MWL，并在逼真的飞行模拟器中使用设计的机场交通模式任务评估其有效性。使用美国国家航空航天局任务负荷指数（NASA-TLX）分数评估并标记各种情况下的感知MWL。然后使用具有2秒滑动时间窗口的快速傅里叶变换提取生理特征。通过比较Kruskal-Wallis（K-W）检验和顺序前向浮动选择（SFFS）的结果进行特征选择。结果证明，最佳输入是所有功率谱密度（PSD）特征。此外，该研究分析了来自不同脑区的脑电图（EEG）特征以及不同MWL水平下PSD变化的影响，以进一步评估所提出系统的性能。对六个分类器进行了10折交叉验证，使用多类K近邻（KNN）分类器对不同MWL水平进行分类时，获得了87.57%的最佳准确率。研究结果表明，基于无线耳机的系统是可靠且可行的。因此，可以开发许多基于无线脑电图设备的系统，用于各种实际驾驶场景。此外，当前系统有助于未来在实际飞行条件下的研究。
