Mental Workload Assessment

Mental Workload (MWL) represents the occupancy of brain resources under working conditions and is one of the important auxiliary strategies for adaptive automation. This article uses different levels of drone simulation tasks to induce different levels of MWL, records the EEG during the control of the drone, and combines the subjective scale to explore the relationship between MWL and EEG and establish MWL assessment model. The study found that the new index closely related to MWL, mutual information. Combining mutual information and power spectrum characteristics to establish a SVM-based MWL assessment model, and finally the accuracy rate of MWL classification reached 92.6%.