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Conference Papers Year : 2022

Drowsiness Detection with a Limited Number of EEG Physiological Signals

Abstract

A variety of studies have already been carried out to try to discriminate the different stages of alertness of a human subject. The purpose of this paper is to propose a method for detecting drowsiness in drivers by adopting a real-time analysis of EEG activity. First, we introduce our database collected at the Technology and Medical Imaging (TIM) laboratory of the University of Monastir, Tunisia. Second, we propose a method for the detection of the decrease of vigilance from a single EEG channel. This method, based on the SVM classifier, was tested on the collected database and allows to detect drowsiness results up to 91.39% in terms of accuracy.
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Dates and versions

hal-04140618 , version 1 (26-06-2023)

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Cite

Rabiaa Lachtar, Slavisa Jovanovic, Khaled Ben Khalifa, Ridha Ben Cheikh, Serge Weber, et al.. Drowsiness Detection with a Limited Number of EEG Physiological Signals. 2022 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS), Jun 2022, Cairo, Egypt. pp.01-05, ⟨10.1109/DTS55284.2022.9809842⟩. ⟨hal-04140618⟩
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