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TURSP-2020 \/ 300), Taif University,Taif, Saudi Arabia"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.<\/jats:p>","DOI":"10.3390\/s21248370","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T21:47:36Z","timestamp":1639604856000},"page":"8370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3221-0371","authenticated-orcid":false,"given":"Ala","family":"Hag","sequence":"first","affiliation":[{"name":"School of Computer Science & Engineering, Taylor\u2019s University, Jalan Taylors, Subang Jaya 47500, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dini","family":"Handayani","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nusa Putra University, Jl. 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Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thulasyammal","family":"Pillai","sequence":"additional","affiliation":[{"name":"School of Computer Science & Engineering, Taylor\u2019s University, Jalan Taylors, Subang Jaya 47500, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teddy","family":"Mantoro","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Sampoerna University, Jakarta 12780, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mun Hou","family":"Kit","sequence":"additional","affiliation":[{"name":"Department of Mechatronic and Biomedical Engineering, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5792-8032","authenticated-orcid":false,"given":"Fares","family":"Al-Shargie","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, American University of Sharjah, Sharjah P.O. 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