  - Background / Introduction: Driver drowsiness is a significant concern and
one of the leading causes of traffic accidents. Advances in cognitive
neuroscience and computer science have enabled the detection of drivers'
drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML).
However, the literature lacks a comprehensive evaluation of drowsiness
detection performance using a heterogeneous set of ML algorithms, and it is
necessary to study the performance of scalable ML models suitable for groups of
subjects. - Methods: To address these limitations, this work presents an
intelligent framework employing BCIs and features based on
electroencephalography for detecting drowsiness in driving scenarios. The
SEED-VIG dataset is used to evaluate the best-performing models for individual
subjects and groups. - Results: Results show that Random Forest (RF)
outperformed other models used in the literature, such as Support Vector
Machine (SVM), with a 78% f1-score for individual models. Regarding scalable
models, RF reached a 79% f1-score, demonstrating the effectiveness of these
approaches. This publication highlights the relevance of exploring a diverse
set of ML algorithms and scalable approaches suitable for groups of subjects to
improve drowsiness detection systems and ultimately reduce the number of
accidents caused by driver fatigue. - Conclusions: The lessons learned from
this study show that not only SVM but also other models not sufficiently
explored in the literature are relevant for drowsiness detection. Additionally,
scalable approaches are effective in detecting drowsiness, even when new
subjects are evaluated. Thus, the proposed framework presents a novel approach
for detecting drowsiness in driving scenarios using BCIs and ML.
