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This paper defines a method to test driver\u2019s fatigue based on the EEG, and different from other researches into fatigue driving, this paper mainly takes the fatigue features of EEG signals in fatigue state and uses wavelet entropy as the feature extraction method to analyze the features of wavelet entropy and spectral entropy features as well as the classification accuracy under the same classifier. The SVM is used to show the classifier\u2019s results. The accuracy of the driver fatigue state monitoring using the wavelet entropy is 90.7%, which is higher than the use of spectral entropy as the characteristic accuracy rate of 81.3%. The results show that the frequency characteristics of EEG can be well applied to driving fatigue testing, but different frequency feature calculation methods will affect the classification accuracy. <\/jats:p>","DOI":"10.1142\/s021800141854023x","type":"journal-article","created":{"date-parts":[[2018,1,19]],"date-time":"2018-01-19T01:10:19Z","timestamp":1516324219000},"page":"1854023","source":"Crossref","is-referenced-by-count":80,"title":["Analysis of Feature Fatigue EEG Signals Based on Wavelet Entropy"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7987-1674","authenticated-orcid":false,"given":"Qingjun","family":"Wang","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China"},{"name":"Shenyang Aerospace University, Shenyang, P. R. 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