为了验证gamma波是否更适用于管制员工作负荷检测，首先设计预实验选取代表性强、区分度大的管制场景场景，在此基础上，设计实验获取管制员在不同工作场景下的脑电（electroencephalogram, EEG）数据及工作负荷数据，并通过S-W（Shapiro-Wilk）检验验证了选取的实验场景的有效性。随后，提出一系列脑电特征并对根据实际情况对脑电数据划分方法做出改进，并对脑电特与工作负荷的相关性进行分析。结果表明：与gamma波相关的特征β + θ + α + γ与工作负荷的相关性最高，可用于管制员的工作负荷检测。最后，选用“留一交叉验证法”建立决策树回归模型对管制员的工作负荷进行检测。检测精度可达0.979 ~ 0.996，证明与gamma波相关的功率谱特征可用于管制员工作负荷的检测。

In order to achieve high-precision detection of controller's workload, firstly, pre-experiments was designed to select representative and distinguishable control scene scenarios, based on which, experiments were designed to obtain the (electroencephalogram) EEG data and workload data of controllers in different work scenarios, and the validity of the selected experimental scenarios was verified by the Shapiro-Wilk (S-W) test. Subsequently, a series of EEG features were proposed and their correlation with workload was analysed. The results show that the correlation between the gamma wave-related indexes β + θ + α + γ and workload is the highest, which can be used for the workload detection of controllers. Finally, the "leave-one-out cross-validation method" was used to establish a decision tree regression model to detect the workload of controllers. The detection accuracy is up to 0.979 ~ 0.996, demonstrating that the power spectrum features associated with gamma waves can be used for controller workload detection.