In order to achieve high-precision detection of controller"s workload, this paper firstly designed pre-experiments to select representative and distinguishable control scene scenarios, based on which, experiments were designed to obtain the EEG data and workload data of controllers in different work scenarios, and the validity of the selected experimental scenarios was verified by the S-W test. Subsequently, a series of EEG features were proposed and their correlation with workload was analysed, and the results showed that the correlation between the gamma wave-related indexes β+θ +α+γ and workload was the highest, which could 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.