{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:04:49Z","timestamp":1773266689602,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T00:00:00Z","timestamp":1693440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development of China","award":["2016YFC0300600"],"award-info":[{"award-number":["2016YFC0300600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>A person\u2019s present mental state is closely associated with the frequency and temporal domain features of spontaneous electroencephalogram (EEG) impulses, which directly reflect neurophysiological signals of brain activity. EEG signals are employed in this study to measure the mental workload of drivers while they are operating a vehicle. A technique based on the quantum genetic algorithm (QGA) is suggested for improving the kernel function parameters of the multi-class support vector machine (MSVM). The performance of the algorithm based on the quantum genetic algorithm is found to be superior to that of other ways when other methods and the quantum genetic algorithm are evaluated for the parameter optimization of kernel function via simulation. A multi-classification support vector machine based on the quantum genetic algorithm (QGA-MSVM) is applied to identify the mental workload of oceanauts through the collection and feature extraction of EEG signals during driving simulation operation experiments in a sea basin area, a seamount area, and a hydrothermal area. Even with a limited data set, QGA-MSVM is able to accurately identify the cognitive burden experienced by ocean sailors, with an overall accuracy of 91.8%.<\/jats:p>","DOI":"10.3390\/bioengineering10091027","type":"journal-article","created":{"date-parts":[[2023,8,31]],"date-time":"2023-08-31T11:31:18Z","timestamp":1693481478000},"page":"1027","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on Mental Workload of Deep-Sea Oceanauts Driving Operation Tasks from EEG Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Xiaoguang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"Shanghai Jiao Tong University and Chiba University International Cooperative Research Center (SJTC-CU-ICRC), Shanghai 200231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Shi","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University and Chiba University International Cooperative Research Center (SJTC-CU-ICRC), Shanghai 200231, China"},{"name":"Institute of Underwater Technology, Shanghai Jiao Tong University, Shanghai 200231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Ye","sequence":"additional","affiliation":[{"name":"China Ship Scientific Research Center, Wuxi 214028, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Underwater Technology, Shanghai Jiao Tong University, Shanghai 200231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Underwater Technology, Shanghai Jiao Tong University, Shanghai 200231, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/978-981-10-4505-9_39","article-title":"Vital Signs Analysis for Oceanauts in Deep Sea Submerged Environment: A Case Study","volume":"64","author":"Miao","year":"2015","journal-title":"Int. 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