Analysis of alpha and theta band to detect driver drowsiness using electroencephalogram (Eeg) signals

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.