{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T19:05:46Z","timestamp":1773947146103,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["RGPIN-2016-04210"],"award-info":[{"award-number":["RGPIN-2016-04210"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user\u2019s mental state considered. However, in real-life situations, different aspects of the user\u2019s state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress\/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI\u2014for example both mental workload and stress level might be related to an aircraft pilot\u2019s risk of error\u2014and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 \u00b1 6.9% and 84.1 \u00b1 5.9%, across 18 participants for mental workload and stress level detection, respectively.<\/jats:p>","DOI":"10.3390\/s22020535","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:33:04Z","timestamp":1641933184000},"page":"535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain\u2013Computer Interface"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3291-3803","authenticated-orcid":false,"given":"Mahsa","family":"Bagheri","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John\u2019s, NL A1C 5S7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5132-2753","authenticated-orcid":false,"given":"Sarah D.","family":"Power","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John\u2019s, NL A1C 5S7, Canada"},{"name":"Faculty of Medicine, Memorial University of Newfoundland, St. John\u2019s, NL A1C 5S7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/2326263X.2015.1008956","article-title":"BNCI Horizon 2020: Towards a roadmap for the BCI community","volume":"2","author":"Brunner","year":"2015","journal-title":"Brain-Comput. 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