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In the paper, a series of evaluation index of security inspection based on the EEG signals of the security officers were proposed to improve the accuracy of dangerous instances detection and decrease the workload of the officers. We performed an experiment to record the EEG data of security officers when they were watching the picture with or without the dangerous item in the uncovered and obscured scenes. Brain network analysis based on graph theory was applied to generate the indexes from the EEG induced by the parcel picture of security inspection, and is a new perspective on the classification of the parcel composition. The paper studied the low-frequency, multi-channel experts EEG signals, calculated the phase locking value (PLV) between every two channels to construct the topological functional brain network (FBN). The appropriate binary FBNs were obtained by setting the thresholds, and then the complex brain network parameters were estimated by the graph-theoretic methods, which were used for classification with 10-fold cross-validation and the average accuracy was 83.3[Formula: see text][Formula: see text][Formula: see text]97.78%. The method was effectively applied to the substance classification and would further improve the recognition accuracy of the target by combining this method with the existing detection technology. <\/jats:p>","DOI":"10.1142\/s021800141950006x","type":"journal-article","created":{"date-parts":[[2018,8,21]],"date-time":"2018-08-21T03:05:58Z","timestamp":1534820758000},"page":"1950006","source":"Crossref","is-referenced-by-count":3,"title":["Experts EEG Cognitive Analysis and Substance Classification in Security Inspection"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5093-2672","authenticated-orcid":false,"given":"Yue","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, School of Information Engineering, Shenyang University, Shenyang 110819, P. R. 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