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This study aims to develop a functional system to assist administrators in identifying and detecting pilots\u2019 real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system\u2019s performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.<\/jats:p>","DOI":"10.3390\/e25071035","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T01:40:02Z","timestamp":1689039602000},"page":"1035","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Detection of Pilot\u2019s Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3568-6439","authenticated-orcid":false,"given":"Chenglin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Chenyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5506-0707","authenticated-orcid":false,"given":"Luohao","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Kun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Haiyue","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Wenbing","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Chaozhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101246","DOI":"10.1016\/j.aei.2021.101246","article-title":"A Systematic Literature Review on Intelligent Automation: Aligning Concepts from Theory, Practice, and Future Perspectives","volume":"47","author":"Ng","year":"2021","journal-title":"Adv. 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