Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography

Drowsiness is a major concern for drivers and one of the leading causes of traffic accidents. Advances in Cognitive Neuroscience and Computer Science have enabled the detection of drivers' drowsiness by using Brain-Computer Interfaces (BCIs) and Machine Learning (ML). Nevertheless, several challenges remain open and should be faced. First, a comprehensive enough evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms is missing in the literature. Last, it is needed to study the detection performance of scalable ML models suitable for groups of subjects and compare it with the individual models proposed in the literature. To improve these limitations, this work presents an intelligent framework that employs BCIs and features based on electroencephalography (EEG) for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to feed different ML regressors and three-class classifiers and then evaluate, analyze, and compare the best-performing models for individual subjects and groups of them. More in detail, regarding individual models, Random Forest (RF) obtained a 78% f1-score, improving the 58% obtained by models used in the literature such as Support Vector Machine (SVM). Concerning scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. The lessons learned can be summarized as follows: i) not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection, and ii) scalable approaches suitable for groups of subjects are effective to detect drowsiness, even when new subjects that are not included in the models training are evaluated.