Fatigue Detection of XYZ Drivers based on Human Brain Wave EEG Signals

  • Shabrina Choirunnisa Politeknik Negeri Jember, Jember, Indonesia
  • Beni Widiawan Politeknik Negeri Jember, Jember, Indonesia
  • Yogiswara Yogiswara Politeknik Negeri Jember, Jember, Indonesia
  • I Gede Wiryawan Politeknik Negeri Jember, Jember, Indonesia
  • Agus Purwadi Politeknik Negeri Jember, Jember, Indonesia
  • Bekti Maryuni Susanto Politeknik Negeri Jember, Jember, Indonesia
DOI: https://doi.org/10.25047/jtit.v9i2.299
Keywords: Early Detection Fatigue, EEG Signals, Fourier Transform, SVM

Abstract

The cause of death due to traffic accidents is
now increasingly common. One of the main factors causing this
accident is driver fatigue. This can happen because the driver
is not aware of his tired mental state. Of course mental fatigue
can cause a lack of concentration while driving. This mental
fatigue can be detected by analyzing the brain waves through
the EEG signal from the driver. This brain wave analysis can
be done by various methods. In this study, the authors
conducted a brain wave-based detection of mental fatigue
using the Fourier transform and Support Vector Machine. The
EEG signal data will be feature extracted using the Fourier
Transform. Then, the results of this extraction will be used for
the classification process with the Support Vector Machine
method. Based on the experimental results, the classification of
mental fatigue using a Support Vector Machine with a linear
kernel obtained an average accuracy of 85%.

Published
2022-12-28
Issue
Vol 9 No 2 (2022)
Section
Articles
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