{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:25:31Z","timestamp":1774945531503,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>Brain stress monitoring has emerged as a critical research area for understanding and managing stress and neurological health issues. This burgeoning field aims to provide accurate information and prediction about individuals' stress levels by analyzing behavioral data and physiological signals. To address this emerging problem, this research study proposes an innovative approach that uses an attention mechanism-based XLNet model (called BrainNet) for continuous stress monitoring and stress level prediction. The proposed model analyzes streams of brain data, including behavioral and physiological signal patterns using Swell and WESAD datasets. Testing on the Swell multi-class dataset, the model achieves an impressive accuracy of 95.76%. Furthermore, when evaluated on the WESAD dataset, it demonstrates even higher accuracy, reaching 98.32%. When applied to the binary classification of stress and no stress using the Swell dataset, the model achieves an outstanding accuracy of 97.19%. Comparative analysis with other previously published research studies underscores the superior performance of the proposed approach. In addition, cross-validation confirms the significance, efficacy, and robustness of the model in brain stress level prediction and aligns with the goals of smart diagnostics for understanding neurological behaviors.<\/jats:p>","DOI":"10.3389\/fncom.2024.1482994","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T04:46:43Z","timestamp":1729745203000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["BrainNet: an automated approach for brain stress prediction utilizing electrodermal activity signal with XLNet model"],"prefix":"10.3389","volume":"18","author":[{"given":"Liao","family":"Xuanzhi","sequence":"first","affiliation":[]},{"given":"Abeer","family":"Hakeem","sequence":"additional","affiliation":[]},{"given":"Linda","family":"Mohaisen","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Umer","sequence":"additional","affiliation":[]},{"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Shrooq","family":"Alsenan","sequence":"additional","affiliation":[]},{"given":"Shtwai","family":"Alsubai","sequence":"additional","affiliation":[]},{"given":"Nisreen","family":"Innab","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.3390\/diagnostics13101690","article-title":"Fetal health state detection using interval type-2 fuzzy neural networks","volume":"13","author":"Abiyev","year":"2023","journal-title":"Diagnostics"},{"key":"B2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/978-3-319-54283-6_3","article-title":"\u201cA wearable system for stress detection through physiological data analysis,\u201d","volume-title":"Proceedings of the Ambient Assisted Living: Italian Forum 2016","author":"Acerbi","year":"2017"},{"key":"B3","first-page":"1","article-title":"\u201cComplex embedded system for stress quantification,\u201d","volume-title":"Proceedings of the 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE)","author":"Adochiei","year":"2019"},{"key":"B4","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1109\/TMSCS.2017.2703613","article-title":"Keep the stress away with soda: stress detection and alleviation system","volume":"3","author":"Akmandor","year":"2017","journal-title":"IEEE Trans.-Multi-Scale Comput. 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