Fast detection method of mental fatigue based on EEG signal characteristics

During the flight in space station, astronauts are prone to mental fatigue, which is the main factor that affects the efficiency of operations and causes errors. For this reason, studying rapid detection methods for human mental fatigue will help ensure the safety of on-orbit operations. The characteristic changes of the electroencephalogram (EEG) can reflect the fatigue state of the brain. Still, the existing EEG method requires multiple lead signals when analyzing mental fatigue, which seriously limits its practical application in the space station environment. This study successfully induced various mental fatigue states in 45 subjects through a foundation experiment using 36 hours of sleep deprivation. Aiming at the non-stationarity of EEG signals, the designed 8-layer db4 wavelet transform structure effectively decomposes δ, θ, α, and β brain rhythm waves. First, screen out the mental fatigue sensitivity characteristics using analysis of variance (ANOVA) and Logistic regression. Secondly, according to the number of sensitive features of mental fatigue, the sharp leads of mental fatigue were further screened out. Finally, the characteristics of 6 keen leaders were used to construct random forest regression models. Finally, the weighted fusion of the regression models at 6 leads to a rapid detection model of mental fatigue, with an average accuracy rate of up to 85.25%.