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. 2011 May;1(2):130-7.

EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests

Zahra Mardi  1 , Seyedeh Naghmeh Miri Ashtiani, Mohammad Mikaili
Affiliations

EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests

Zahra Mardi et al. J Med Signals Sens. 2011 May.

Abstract

Electro encephalography (EEG) is one of the most reliable sources to detect sleep onset while driving. In this study, we have tried to demonstrate that sleepiness and alertness signals are separable with an appropriate margin by extracting suitable features. So, first of all, we have recorded EEG signals from 10 volunteers. They were obliged to avoid sleeping for about 20 hours before the test. We recorded the signals while subjects did a virtual driving game. They tried to pass some barriers that were shown on monitor. Process of recording was ended after 45 minutes. Then, after preprocessing of recorded signals, we labeled them by drowsiness and alertness by using times associated with pass times of the barriers or crash times to them. Then, we extracted some chaotic features (include Higuchi's fractal dimension and Petrosian's fractal dimension) and logarithm of energy of signal. By applying the two-tailed t-test, we have shown that these features can create 95% significance level of difference between drowsiness and alertness in each EEG channels. Ability of each feature has been evaluated by artificial neural network and accuracy of classification with all features was about 83.3% and this accuracy has been obtained without performing any optimization process on classifier.

Keywords: Alertness; drowsy driving; electro encephalography; fractal dimensions; two-tailed t-test.

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Conflict of interest statement

Conflict of Interest: None declared

Figures

Figure 1
Figure 1
Placement of recorded channels in defined montage
Figure 2
Figure 2
Designed virtual driving game for data collection. (a) In this figure the barrier comes near to the subject and subject should try to pass it. (b) Subject has crashed to the barrier. Crash time has been shown on monitor
Figure 3
Figure 3
Steps for labeling of two seconds epochs of recorded EEG by usage of times for pass and crash that was saved in driving game's output file for each subject
Figure 4
Figure 4
Mean and variance of Higuchi's fractal dimension. (a) Mean of Higuchi's fractal dimension of all observations in each channel for drowsy and alert. (b) Box plot of Higuchi's fractal dimension over trials. In this Figure mean values and variance of all observations in alertness and drowsiness in each channel can be seen
Figure 5
Figure 5
Mean and variance of Petrosian's fractal dimension. (a) Mean of Petrosian's fractal dimension of all observations in each channel for drowsy and alert. (b) Box plot of Petrosian's fractal dimension over trials. In this figure mean values and variance of all observations in alertness and drowsiness in each channel have been shown
Figure 6
Figure 6
Mean and variance of logarithm of energy. (a) Mean of logarithm of energy of all observations in each channel for drowsy and alert. (b) Box plot of logarithm of energy over trials. In this figure mean values and variance of all observations in alertness and drowsiness in each channel can be seen
See this image and copyright information in PMC

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