Characterization of EEG signals based on brain fatigue

Brain fatigue can lead to a decrease in an individual's overall performance, and sustained mental effort is one of the health risks that can be easily overlooked. In order to effectively detect brain fatigue and validate fatigue characteristics, in this study, the EEG signals of 10 subjects at the beginning, middle and end of the task were captured using a multichannel portable EEG instrument and denoised using a fusion algorithm of empirical modal decomposition and fast independent component analysis. Analysis of the power spectral density and energy spectrum of the EEG signals, combined with the experimental results of the fatigue index value F, showed that there was a strong correlation between the F and O brain regions with δ-waves, the AF, F, and FC brain regions with θ-waves, the F, P, and O brain regions with α-waves, and the F and T brain regions with β-waves (P < 0. 001). By two-tailed (Sig < 0. 05) analysis, it was found that there were significant changes in α-waves in frontal and occipital lobes, and θ-waves in frontal and central lobes. It is hoped that this will provide data for the effective detection of fatigue EEG and further improve the fatigue recognition rate.