Electroencephalography Analysis Frameworks for the Driver Fatigue Problem: A Benchmarking Study

Kemalcan Kucuk, Efe Ismet Yurteri, Beren Semiz

2025

Abstract

Driver fatigue problem is a major factor contributing to traffic accidents globally, making its analysis and detection crucial for early prevention. Among various approaches for detecting driver fatigue, electroencephalography (EEG) processing is one of the most widely employed techniques. This study investigates different feature extraction and machine learning methodologies for detecting driver fatigue using EEG signals, and provides a comparative performance analysis against existing methods. To that aim, we used a publicly available dataset collected during a simulated driving task and applied our feature extraction methods to the concurrently recorded EEG signals. Various features from distinct groups were extracted to serve as the foundation for subsequent analyses. The 30 channels from the original dataset were individually evaluated based on the performance of machine learning algorithms trained on each channel, allowing for the selection of the four most optimal channels. Using these selected channels, the different subsets of extracted features were then compared based on their accuracy values. For further analysis, the features were ranked using both ANOVA and Chi-Squared feature selection methods to examine the impact of the number of features on model performance. Each model was first trained using a standard training-testing split, where the highest-scoring model was a Support Vector Machine (SVM) achieving a test accuracy of 90.73%. Additionally, using a Leave-One-Out Cross-Validation (LOOCV) approach, the highest performing model was found to be the k-Nearest Neighbors (K-NN) classifier with an average test accuracy of 70.45%. The analyses and comparisons presented in this study may serve as a basis for developing real-time applications and for further in-depth investigations.

Paper Citation

in Harvard Style

Kucuk K., Yurteri E. and Semiz B. (2025). Electroencephalography Analysis Frameworks for the Driver Fatigue Problem: A Benchmarking Study. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-731-3, SciTePress, pages 829-836. DOI: 10.5220/0013086900003911

in Bibtex Style

@conference{biosignals25,
author={Kemalcan Kucuk and Efe Yurteri and Beren Semiz},
title={Electroencephalography Analysis Frameworks for the Driver Fatigue Problem: A Benchmarking Study},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={829-836},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013086900003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Electroencephalography Analysis Frameworks for the Driver Fatigue Problem: A Benchmarking Study
SN - 978-989-758-731-3
AU - Kucuk K.
AU - Yurteri E.
AU - Semiz B.
PY - 2025
SP - 829
EP - 836
DO - 10.5220/0013086900003911
PB - SciTePress