Noise Robustness Analysis of Performance for EEG-Based Driver Fatigue Detection Using Different Entropy Feature Sets

Jianfeng Hu,Ping Wang

doi:10.3390/e19080385

Abstract

Driver fatigue is an important factor in traffic accidents, and the development of a detection system for driver fatigue is of great significance. To estimate and prevent driver fatigue, various classifiers based on electroencephalogram (EEG) signals have been developed; however, as EEG signals have inherent non-stationary characteristics, their detection performance is often deteriorated by background noise. To investigate the effects of noise on detection performance, simulated Gaussian noise, spike noise, and electromyogram (EMG) noise were added into a raw EEG signal. Four types of entropies, including sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for feature sets. Three base classifiers (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT)) and two ensemble methods (Bootstrap Aggregating (Bagging) and Boosting) were employed and compared. Results showed that: (1) the simulated Gaussian noise and EMG noise had an impact on accuracy, while simulated spike noise did not, which is of great significance for the future application of driver fatigue detection; (2) the influence on noise performance was different based on each classifier, for example, the robust effect of classifier DT was the best and classifier SVM was the weakest; (3) the influence on noise performance was also different with each feature set where the robustness of feature set FE and the combined feature set were the best; and (4) while the Bagging method could not significantly improve performance against noise addition, the Boosting method may significantly improve performance against superimposed Gaussian and EMG noise. The entropy feature extraction method could not only identify driver fatigue, but also effectively resist noise, which is of great significance in future applications of an EEG-based driver fatigue detection system.

Highlights

As EEG signals can reflect the instant state of the brain, it is an excellent method to evaluate the state and function of the brain, and is often used to assist in the diagnosis of stroke, epilepsy, and seizure
DT was the best and classifier SVM was the weakest; (3) the influence on noise performance was different with each feature set where the robustness of feature set fuzzy entropy (FE) and the combined feature set were the best; and (4) while the Bootstrap Aggregating (Bagging) method could not significantly improve performance against noise addition, the Boosting method may significantly improve performance against superimposed
When all EEG channels are used for detecting driver fatigue, good results may be achieved; we wanted to understand what the impact would be on detection performance if the noise was superimposed on some channels

Summary

Introduction

As EEG signals can reflect the instant state of the brain, it is an excellent method to evaluate the state and function of the brain, and is often used to assist in the diagnosis of stroke, epilepsy, and seizure. Various computational methods based on EEG signals have been developed for the analysis and detection of driver fatigue. Correa et al [1] developed an automatic method to detect the drowsiness stage in EEG signals using 19 features and a Neural Network classifier, and obtained an accuracy of 83.6% for drowsiness detections. Mu et al [2] employed fuzzy entropy for feature extraction and an SVM classifier to achieve an average accuracy of 85%. Other results from their study showed that four feature sets (SE, AE, PE, and FE) and SVM were proposed, with an average accuracy of 98.75% [3]. Fu et al [4]

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Results