Controller Workload Detection Based on Decision Tree Model
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(electroencephalo-gram)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.
万方数据
维普
