Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals.

Jianfeng Hu

doi:10.3389/fncom.2017.00072

Abstract

Purpose: Driving fatigue has become one of the important causes of road accidents, there are many researches to analyze driver fatigue. EEG is becoming increasingly useful in the measuring fatigue state. Manual interpretation of EEG signals is impossible, so an effective method for automatic detection of EEG signals is crucial needed.Method: In order to evaluate the complex, unstable, and non-linear characteristics of EEG signals, four feature sets were computed from EEG signals, in which fuzzy entropy (FE), sample entropy (SE), approximate Entropy (AE), spectral entropy (PE), and combined entropies (FE + SE + AE + PE) were included. All these feature sets were used as the input vectors of AdaBoost classifier, a boosting method which is fast and highly accurate. To assess our method, several experiments including parameter setting and classifier comparison were conducted on 28 subjects. For comparison, Decision Trees (DT), Support Vector Machine (SVM) and Naive Bayes (NB) classifiers are used.Results: The proposed method (combination of FE and AdaBoost) yields superior performance than other schemes. Using FE feature extractor, AdaBoost achieves improved area (AUC) under the receiver operating curve of 0.994, error rate (ERR) of 0.024, Precision of 0.969, Recall of 0.984, F1 score of 0.976, and Matthews correlation coefficient (MCC) of 0.952, compared to SVM (ERR at 0.035, Precision of 0.957, Recall of 0.974, F1 score of 0.966, and MCC of 0.930 with AUC of 0.990), DT (ERR at 0.142, Precision of 0.857, Recall of 0.859, F1 score of 0.966, and MCC of 0.716 with AUC of 0.916) and NB (ERR at 0.405, Precision of 0.646, Recall of 0.434, F1 score of 0.519, and MCC of 0.203 with AUC of 0.606). It shows that the FE feature set and combined feature set outperform other feature sets. AdaBoost seems to have better robustness against changes of ratio of test samples for all samples and number of subjects, which might therefore aid in the real-time detection of driver fatigue through the classification of EEG signals.Conclusion: By using combination of FE features and AdaBoost classifier to detect EEG-based driver fatigue, this paper ensured confidence in exploring the inherent physiological mechanisms and wearable application.

Highlights

Electroencephalogram (EEG) is a very important monitoring technique to reflect the instantaneous state of the brain
fuzzy entropy (FE) feature set performs slightly better than the combined entropy (FE + sample entropy (SE) + approximate entropy (AE) + Spectral entropy (PE)) feature set (0.020 against 0.029)
It can be seen that the FE feature set performs about 0.098 and 0.075 better than the SE and AE feature set at Error rate (ERR) index, respectively

Summary

Introduction

Electroencephalogram (EEG) is a very important monitoring technique to reflect the instantaneous state of the brain. Many EEG-based studies have been performed to analyze and detect driving fatigue (Kar et al, 2010; Mu et al, 2017a; Yin et al, 2017). Li et al achieved the highest accuracy of 91.5% based on 12 types of energy parameters (Li et al, 2012). Fu et al reached a highest accuracy of 92.5% based on Hidden Markov Model (HMM; Fu et al, 2016). Zhao et al hit a higher accuracy (98.7%) based on a KPCA-SVM classifier (Zhao et al, 2010). AE is biased because it includes self-matching in the count, while SE needs to avoid the log(0) problem. They are very sensitive to input parameters. FE is based on a continuous function to compute the dissimilarity between two zero-mean subsequences, so it is more stable in noise and parameter initialization

Methods

Results

Conclusion