{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:58:57Z","timestamp":1791464337957,"version":"4.3.4"},"reference-count":0,"publisher":"Kare Publishing","issue":"48","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/jcpres.com\/storage\/upload\/pdfs\/1791460424-en.pdf"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Objective: Fatigue is a significant occupational health issue that adversely affects attention and decision-making. This study aimed to objectively predict fatigue levels using EEG signals and machine learning models and to develop a physiologically based assessment framework with potential future applications in nursing.\n\nMaterials and Methods: Time-domain, frequency-domain, entropy, Hjorth, and wavelet features were extracted from EEG data collected from 12 participants aged 21\u201340 years, with fatigue levels labeled using various scales.\n\nResults: Classification performance varied across validation strategies. Under 5-fold cross-validation, Random Forest achieved the highest performance (accuracy=1.00, macro-F1=1.00), while KNN and NB also demonstrated strong performance. However, under LOOCV, sensitivity, precision, and macro-F1 values decreased despite relatively high accuracy. LOSO validation yielded more balanced results, with Random Forest maintaining the best performance (accuracy=0.97, macro-F1=0.91).\n\nConclusion: EEG-derived features provide valuable information for fatigue assessment. Although high performance was achieved under certain validation strategies, subject-independent evaluation revealed more realistic performance levels. The proposed framework offers a practical approach to fatigue monitoring. Future studies should focus on larger, domain-specific datasets and multimodal integration to enhance generalizability.<\/jats:p>","DOI":"10.14744\/cpr.2026.17486","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:26:41Z","timestamp":1791462401000},"page":"530","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Based Fatigue Detection From EEG Signals: A Methodological Pilot Framework With Potential For Nursing Applications"],"prefix":"10.14744","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0900-6025","authenticated-orcid":false,"given":"Serife","family":"Cetin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7065-9829","authenticated-orcid":false,"given":"Eda","family":"Dagdevir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5966","published-online":{"date-parts":[[2026,10,8]]},"container-title":["Journal of Clinical Practice and Research"],"original-title":[],"link":[{"URL":"https:\/\/jcpres.com\/storage\/upload\/pdfs\/1791460424-en.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:26:42Z","timestamp":1791462402000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcpres.com\/article\/2873"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,8]]},"references-count":0,"journal-issue":{"issue":"48","published-online":{"date-parts":[[2026,10,8]]}},"URL":"https:\/\/doi.org\/10.14744\/cpr.2026.17486","relation":{},"ISSN":["2980-2156"],"issn-type":[{"value":"2980-2156","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,8]]}}}