Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Driver Drowsiness Detection using Zero Crossing Rate Feature Extracted from EEG Signals Recorded by Consumer-Grade Headsets

Document Type : Full Research Paper

Authors

Ashkan Giveki 1
Seyed-Kamaledin Setarehdan 2

1 M.Sc., School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 Professor, Biomedical Engineering Group, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

https://doi.org/10.22041/ijbme.2024.2037251.1916
Abstract
Driver drowsiness is recognized as one of the leading causes of accidents and road incidents globally. In recent years, considerable efforts have been directed towards developing effective tools for detecting and predicting driver drowsiness using cost-effective methods suitable for public use. Biological signals, particularly electroencephalogram (EEG), have become highly valued for their immediate reflection of drowsiness-induced changes in detecting driver alertness and fatigue. Despite significant advancements in drowsiness detection, researchers continue to strive for enhanced accuracy in detection models by extracting novel and relevant features from EEG signals. In this study, we introduce a new set of features derived from EEG signals and develop an algorithm to predict driver drowsiness using these features. The dataset used in this study was gathered from 50 volunteers during driving activity in the Nasir driving simulator, employing consumer-grade headsets Muse 2 and Muse S to record EEG signals. Following preprocessing and segmentation of the signals into 30-second epochs, features including the zero-crossing rate of the original signal, as well as its first and second derivatives, were extracted. Statistical analysis identified 11 features which exhibited significant differences between the states of alertness and drowsiness. These features were then utilized to develop a predictive model employing KNN and SVM classifiers. The maximum accuracy achieved in predicting driver drowsiness using the proposed algorithm was 86.37%. Hence, the introduced features are proposed as effective parameters for enhancing the accuracy of drowsiness prediction.

Keywords

Driver Drowsiness
EEG
Zero Crossing Rate
Muse Headsets
Real-Time Detection
Driving Simulator
ORD

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 17, Issue 4
Winter 2024
Pages 373-387

  • Receive Date 01 August 2024
  • Revise Date 31 August 2024
  • Accept Date 23 September 2024

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