Drowsiness Detection using Minimum Discriminated Features and Single EEG Channel

Authors

  • 1Iraqi commission for computers and informatics, Informatics Institute of Postgraduate Studies, Iraq, Baghdad.
  • University of Thi-Qar, College of Education for Pure Science, Iraq.
  • University of Information Technology & Communication, Iraq, Baghdad

DOI:

https://doi.org/10.32792/jeps.v13i1.242

Keywords:

Drowsy driving detection, Electroencephalography, wavelet transform.

Abstract

Driver drowsiness is one of the top causes of road fatalities and transportation industry risks.
Electroencephalography (EEG) has been regarded as one of the most reliable physiological signs for
detecting drowsiness in drivers since it directly assesses neurophysiological brain activity. If possible, a
system that monitors driver drowsiness and warnings can prevent the greatest number of traffic incidents.
This study presents a simple and inexpensive method for detecting driver drowsiness or sleep onset with
single-channel EEG signal processing. The primary contribution of this study is the identification of
sleepiness detection from a publicly available graph signal dataset using only two discriminated features
(standard deviation and entropy) and a filter that is easily implementable in any microcontroller device or
smartphone. This study employed the least square support vector machine to categorize driver status into
two groups (awake or drowsy). The proposed method can be implemented in real-time with high
efficiency and precision. Furthermore, this method can be readily applied on a smartphone to create and
expand a sleepiness detection and alert system for vehicle drivers. The trial findings indicate an accuracy
of 95.5 percent.

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Published

2023-04-05

Issue

Vol. 13 No. 1 (2023): Journal of Education for Pure Science- University of Thi-Qar

Section

Articles

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