Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning

• Published in: Computers in biology and medicine Vol. 184; p. 109405
• Main Authors: Yeh, Pin-Yang, Sun, Cheuk-Kwan, Sue, Yu-Ru
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2025; Elsevier Limited
• Subjects: Addictions; Addictive behaviors; Adult; Adults; Alcohol use; Alcohol use disorder; Alcoholism - physiopathology; Algorithms; Asymmetry; Attention deficit hyperactivity disorder; Biomarkers; Brain research; Cronbach's alpha; Decision trees; Driving conditions; Driving Under the Influence; Drunkenness; EEG; Electrodes; Electroencephalography; Electroencephalography - methods; Female; Humans; Internal Medicine; Learning algorithms; Machine Learning; Male; Mental depression; Middle Aged; Neuropsychology; Other; Questionnaires; Signal processing; Signal Processing, Computer-Assisted; Support vector machines; Theta/beta ratio; Traffic; Virtual reality; Theta/beta ratio; Alcohol use disorder; Asymmetry; Driving under the influence; Machine learning

Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algorithms revealed a very high accuracy of decision trees with neuropsychological features in predicting the risk of DUIA despite limited data availabilit...