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
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109405

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 availability. Thus, this study aimed at comparing six well-known ML algorithms based on electroencephalographic (EEG) signals to differentiate adults with AUD and DUIA (AUD-DD) from those with AUD without DUIA (AUD-NDD) and controls. Fifteen AUD-DD and 10 AUD-NDD participants were recruited from a single tertiary referral center. Fourteen social drinkers without DUIA served as controls. Their EEG signals related to driving conditions were gathered using a VR headset with eight electrodes (F3, F4, Fz, C3, C4, Cz, P3, and P4). Based on the labeled features of EEG asymmetry and theta/beta ratio (TBR), comparisons between different algorithms were conducted. Fz and Cz electrodes exhibited differences in TBR across the three groups (all p < 0.02), while there were no significant differences between AUD-DD individuals and social drinkers. In contrast, asymmetries of between-group differences were not observed (all p > 0.09). K-nearest neighbors (KNN) with TBR showed the highest accuracy (83 %) in distinguishing AUD-DD individuals from controls, while logistic regression (LR), support vector machines (SVM), and naive Bayes (NB) with EEG asymmetric features demonstrated high accuracy in identifying DUIA (all 80 %) in AUD adults. LR, SVM, and NB with asymmetry may be employed in predicting DUIA among AUD adults, while KNN with TBR may be used for identifying DUIA in the general population. •K-Nearest Neighbors with theta/beta ratio is effective in identifying the risk of drunk driving in the general population.•Logistic Regression, Support Vector Machines, and Naive Bayes with EEG asym. had equal accuracy in detecting drunk driving in Alcoholism.•The theta/beta ratio at Fz and Cz electrodes, but not EEG asymmetry, showed between-group differences.