Early driver drowsiness detection using electroencephalography signals

Abstract

This study aims to provide a solution in determining the drowsiness state among drivers at the early stage. The revolving issue nowadays is the increasing number of traffic crashes due to drowsiness are considerably at an alarming stage. Drowsiness is a state of sleepiness, which leads to the lapse of attention and focuses. Numerous factors caused drowsiness, which can be determined through the biosignals of an individual. A thorough analysis of the bio-signals of drivers, which is the electroencephalogram (EEG), is applied as one of the solutions in handling drowsiness. EEG is significant in measuring drowsiness levels as it shows the electrical activity of the brain. This study analyzes driver behaviour by measuring the brain wave pattern to detect drowsiness. In this study, the brain signals from the subjects were collected using an EEG headset interfaced with the OpenBCI software. The subjective approach, namely, the Karolinska Sleepiness Scale (KSS), is performed to validate the data. This study involves signal processing in examining brain wave patterns by using MATLAB. An alpha frequency band is extracted from the estimation of power spectral density (PSD) using the periodogram method. Classification of all the extracted features by using a decision tree showed high accuracy ranges from 77.1%-97.20% for each of the subjects. Drowsiness managed to be determined based on increasing alpha power.