{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:29:03Z","timestamp":1763990943831,"version":"3.45.0"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Drowsy driving is a major contributor to road accidents, as reduced vigilance degrades situational awareness and reaction control. Reliable assessment of alertness versus drowsiness can therefore support accident prevention. Key gaps remain in physiology-based detection, including robust identification of microsleep and transient vigilance shifts, sensitivity to fatigue-related changes, and resilience to motion-related signal artifacts; practical sensing solutions are also needed. Using Electroencephalogram (EEG) recordings from the MIT-BIH Polysomnography Database (18 records; &gt;80 h of clinically annotated data), we framed wakefulness\u2013drowsiness discrimination as a binary classification task. From each 30 s segment, we extracted 61 handcrafted features spanning linear, nonlinear, and frequency descriptors designed to be largely robust to signal-quality variations. Three classifiers were evaluated\u2014k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT)\u2014alongside a DT-based bagging ensemble. KNN achieved 99% training and 80.4% test accuracy; SVM reached 80.0% and 78.8%; and DT obtained 79.8% and 78.3%. Data standardization did not improve performance. The ensemble attained 100% training and 84.7% test accuracy. While these results indicate strong discriminative capability, the training\u2013test gap suggests overfitting and underscores the need for validation on larger, more diverse cohorts to ensure generalizability. Overall, the findings demonstrate the potential of machine learning to identify vigilance states from EEG. We present an interpretable EEG-based classifier built on clinically scored polysomnography and discuss translation considerations; external validation in driving contexts is reserved for future work.<\/jats:p>","DOI":"10.3390\/computers14120509","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T13:09:25Z","timestamp":1763989765000},"page":"509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of Drowsiness and Alertness States Using EEG Signals to Enhance Road Safety: A Comparative Analysis of Machine Learning Algorithms and Ensemble Techniques"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3279-7703","authenticated-orcid":false,"given":"Masoud","family":"Sistaninezhad","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Seraj University, Tabriz 5137894797, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saman","family":"Rajebi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Seraj University, Tabriz 5137894797, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2974-1801","authenticated-orcid":false,"given":"Siamak","family":"Pedrammehr","sequence":"additional","affiliation":[{"name":"Faculty of Design, Tabriz Islamic Art University, Tabriz 5164736931, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2043-5437","authenticated-orcid":false,"given":"Arian","family":"Shajari","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3395-1772","authenticated-orcid":false,"given":"Hussain Mohammed","family":"Dipu Kabir","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Cyber Futures Institute, Charles Sturt University, Bathurst, NSW 2795, Australia"},{"name":"Rural Health Research Institute, Charles Sturt University, Orange, NSW 2800, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7354-260X","authenticated-orcid":false,"given":"Thuong","family":"Hoang","sequence":"additional","affiliation":[{"name":"Faculty of Science Engineering and Built Environment, Deakin University, Waurn Ponds, VIC 3216, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5487-0237","authenticated-orcid":false,"given":"Stefan","family":"Greuter","sequence":"additional","affiliation":[{"name":"School of Communication and Creative Arts, Deakin University, Burwood, VIC 3125, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houshyar","family":"Asadi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2417","DOI":"10.48084\/etasr.1615","article-title":"Identification of risk factors influencing road traffic accidents","volume":"8","author":"Touahmia","year":"2018","journal-title":"Eng. 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