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Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper, we propose a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network. Features including approximate entropy (AE), fuzzy entropy (FE), R\u00e9nyi entropy (RE), differential entropy (DE), and multi-scale entropy (MSE) are first calculated to study dynamic emotional information. Then, we train a BiLSTM classifier with the inputs of entropy features to identify different emotions. Our results show that MSE of EEG is more efficient than other single-entropy features in recognizing emotions. The performance of BiLSTM is further improved with an accuracy of 70.05% using fused entropy features compared with that of single-type feature.<\/jats:p>","DOI":"10.3390\/e24091281","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T22:53:46Z","timestamp":1663023226000},"page":"1281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0828-9748","authenticated-orcid":false,"given":"Xin","family":"Zuo","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China"},{"name":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, 40014 Jyv\u00e4skyl\u00e4, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0865-5215","authenticated-orcid":false,"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China"},{"name":"Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4168-9102","authenticated-orcid":false,"given":"Timo","family":"H\u00e4m\u00e4l\u00e4inen","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, 40014 Jyv\u00e4skyl\u00e4, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanbing","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengyu","family":"Cong","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China"},{"name":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, 40014 Jyv\u00e4skyl\u00e4, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., and Yang, X. 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