Research on Objective Vigilance Detection and Channel Selection Techniques Based on EEG
During spaceflight,the diminished vigilance can impair the efficiency of astronauts and it is also a potential risk factor for major safety accidents.In this paper,the objective vigilance detec-tion technologies tailored for application in aerospace contexts were explored.An alertness modeling experiment combining multiple rounds of the Psychomotor Vigilance Task(PVT)and 3-back tasks was designed,and the EEG signals and behavioral data were continuously collected.Data analysis indicated that the experimental paradigm effectively induced a reduction in alertness among 37 sub-jects,marked by a diminishment in fast wave components and an augmentation in slow wave compo-nents of the brain;there was a decrease in complexity,and pronounced alterations were observed in the frontal,temporal,and occipital lobes.Using Analysis of Variance(ANOVA)combined with a Support Vector Machine(SVM)for single-feature modeling,6 alertness-sensitive features were screened out.Subsequently,the SVM-RFECV technique was utilized to select 12 alertness-sensitive leads,and an objective alertness detection model was constructed using SVM.The results showed that the average classification accuracy was 83.10%before feature selection and increased to 87.16%after lead selection,with an improvement of about 4%.It indicates that feature selection and lead selection can effectively improve the overall performance of the model,simplify the EEG acquisition process,and reduce the workload and time cost of subsequent data processing.
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