Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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EEG-Based Driver Drowsiness Detection Using Machine Learning Classifiers For Enhanced Road Safety

Authors
Chiranjevulu Divvala1, *, Eppili Jaya1, D. Udaya1, V. Sai Damodar Rao1, P. Sai Srinivas1, A. Sunil1
1Aditya Institute of Technology and Management, Tekkali, AP, India, 532201
*Corresponding author. Email: chiru.divvala@gmail.com
Corresponding Author
Chiranjevulu Divvala
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_62How to use a DOI?
Keywords
Driver Drowsiness Detection; EEG; Feature Selection; Machine Learning; Road Safety
Abstract

Driver drowsiness is one of the major causes of traffic accidents worldwide. Detecting fatigue early is essential for preventing accidents. In this study, electroencephalography (EEG) is used to capture brain signals and a suite of machine learning classifiers—including LightGBM, XGBoost, Extra Trees, Random Forest, Gradient Boosting, AdaBoost, Decision Trees, K-Nearest Neighbors, Logistic Regression, Ridge Classifier, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and SVM with a linear kernel—is employed to classify drivers’ alert and drowsy states. Key features including various brain wave frequencies (delta, theta, alpha, beta, and gamma) along with attention and meditation parameters are extracted and used for model training. The performance is evaluated through classification accuracy, ROC curves, confusionmatrices, and cross-validation metrics.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_62How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Chiranjevulu Divvala
AU  - Eppili Jaya
AU  - D. Udaya
AU  - V. Sai Damodar Rao
AU  - P. Sai Srinivas
AU  - A. Sunil
PY  - 2025
DA  - 2025/11/04
TI  - EEG-Based Driver Drowsiness Detection Using Machine Learning Classifiers For Enhanced Road Safety
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 726
EP  - 742
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_62
DO  - 10.2991/978-94-6463-858-5_62
ID  - Divvala2025
ER  -