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Based on six types of EEG features from 12 subjects, mean F1 score of 0.937, 0.936 and 0.968 are achieved using Logistic Regression, Support Vector Machine and RUSBoost classifier, respectively. This promising result indicates the high distinctive characteristics in music-induced autobiographical memory EEG, which is suitable for identity authentication applications. <\/jats:p>","DOI":"10.1142\/s0218126622502012","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T07:13:51Z","timestamp":1649920431000},"source":"Crossref","is-referenced-by-count":1,"title":["Identity Authentication Based on Music-Induced Autobiographical Memory EEG"],"prefix":"10.1142","volume":"31","author":[{"given":"Xin","family":"Xu","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing 210003, P. R. 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