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In order to realize the intelligent recognition of fatigue state, a fatigue recognition health management scheme based on wearable EEG is proposed. The first problem to be solved is which frequency of brainwave affects or presents the human fatigue state. The study used Wilcoxon rank sum test, chi-square test, CART classification tree and probit model to analyze δ brain waves (1~3 Hz), θ brain waves (4~7 Hz), α brain waves (8~13 Hz), β. The relationship between brain waves (14~30 Hz) and fatigue. The study found that α wave, β wave, θ wave and (α + θ) / β, β / α have significant differences in fatigue and non-fatigue state. These brainwave variables can show differences in human fatigue status and help identify fatigue states. [ABSTRACT FROM AUTHOR]