Automatic Drowsiness Detection based on a Single Channel of EEG Signals using a Hybrid Analysis and Decision Tree Classification Method under Python

Mouad Elmouzoun Elidrissi, Lhoucine Ben Taleb, Lyazidi Aissam, Mourad Rattal, Azeddine Mouhsen, Mohammed Harmouchi, Essoukaki Elmaati

2021

Abstract

The human brain generates millions of signals because they translate all our movements and thoughts, our physical and psychological state. During driving, all these signals are generated simultaneously. Vigilance while being behind the wheel is necessary. However, when the roads are monotonous, especially when taking highways, that alertness state decreases, and the drowsiness state shows off. In Morocco, 1/3 of fatal accidents on the highway are caused by drowsiness and sleeping while driving. Wherefore, we proposed the idea of developing an automatic system based on electroencephalogram signals (EEG) that can predict the state of drowsiness in real-time using several features extracted from the EEG records when this state occurs to drivers while driving. The proposed work is based on time-frequency analysis of EEG signals based on one singlechannel (FP1-Ref), and drowsiness is predicted using machine learning techniques under Python. The results are significant where we could reach an accuracy of prediction with an average of 95.7% within 0.062 seconds using the decision tree classification method.

Paper Citation

in Harvard Style

Elmouzoun Elidrissi M., Ben Taleb L., Aissam L., Rattal M., Mouhsen A., Harmouchi M. and Elmaati E. (2021). Automatic Drowsiness Detection based on a Single Channel of EEG Signals using a Hybrid Analysis and Decision Tree Classification Method under Python. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 301-305. DOI: 10.5220/0010732900003101

in Bibtex Style

@conference{bml21,
author={Mouad Elmouzoun Elidrissi and Lhoucine Ben Taleb and Lyazidi Aissam and Mourad Rattal and Azeddine Mouhsen and Mohammed Harmouchi and Essoukaki Elmaati},
title={Automatic Drowsiness Detection based on a Single Channel of EEG Signals using a Hybrid Analysis and Decision Tree Classification Method under Python},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={301-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010732900003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Automatic Drowsiness Detection based on a Single Channel of EEG Signals using a Hybrid Analysis and Decision Tree Classification Method under Python
SN - 978-989-758-559-3
AU - Elmouzoun Elidrissi M.
AU - Ben Taleb L.
AU - Aissam L.
AU - Rattal M.
AU - Mouhsen A.
AU - Harmouchi M.
AU - Elmaati E.
PY - 2021
SP - 301
EP - 305
DO - 10.5220/0010732900003101