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

TL;DR

This study evaluates various machine learning models, especially scalable ones like Random Forest, using EEG data to improve driver drowsiness detection, which is crucial for reducing traffic accidents caused by fatigue.

Contribution

The paper introduces a comprehensive framework employing BCIs and scalable ML models for drowsiness detection, highlighting the effectiveness of Random Forest in both individual and group scenarios.

Findings

Random Forest achieved 78% f1-score for individuals

Scalable models like RF reached 79% f1-score for groups

Exploration of diverse ML algorithms enhances drowsiness detection

Abstract

- 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.