Analysis of Alpha and Theta Band to Detect Driver
Drowsiness Using Electroencephalogram (EEG)
Signals
Pradeep Kumar Sivakumar1,
Jerritta Selvaraj1, Krishnakumar Ramaraj2, and Arun Sahayadhas1
1Artificial
Intelligence research Lab, Vels Institute of Science Technology and Advanced
Studies, India
2Department of Electrical and Electronics
Engineering, Vels Institute of Science Technology and Advanced Studies, India
Abstract: Driver drowsiness is recognized as a leading
cause for crashes and road accidents in the present day. This paper presents an
analysis of Alpha and Theta band for drowsinesss detection using
Electroencephalogram (EEG) signals. The EEG signal of 21 channels is acquired
from 10 subjects to detect drowsiness. The Alpha and Theta bands of raw EEG
signal are filtered to remove noises and both linear and non-linear features
were extracted. The feature Hurst and kurtosis shows the significant difference
level (p<0.05) for most of the channels based on Analysis of Variance (ANOVA)
test. So, they were used to classify the drowsy and alert states using Quadratic
Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA) and K- Nearest
Neighbour (KNN) classifiers. In the case of Alpha band, the channels F8 and T6 achieved
a maximum accuracy of 92.86% using Hurst and the channel T5 attained 100%
accuracy for kurtosis. In the case of Theta band, Hurst achieved 100% accuracy
for the channel F8 and Kurtosis obtained a maximum accuracy of 92.85% in the
channels FP1, CZ and O1. A comparison between Alpha and Theta band for the
various channels using KNN Classifier was done and the results indicate that the
selected channels from Alpha and Theta bands can be used to detect drowsiness and
alert the driver.
Keywords: Electroencephalogram, alpha band, theta
band, drowsiness, KNN, ANOVA.
Received April 19,
2020; accepted December 1, 2020