Sample-Entropy-Based Method for Real Driving Fatigue Detection with Multichannel Electroencephalogram

Abstract

Safe driving plays a crucial role in public health, and driver fatigue causes a large proportion of crashes in road driving. Hence, this paper presents the development of an efficient system to determine whether a driver is fatigued during real driving based on 14-channel EEG signals. The complexity of the EEG signal is then quantified with the sample entropy method. Finally, we explore the performance of multiple kernel-based algorithms based on sample entropy features for classifying fatigue and normal subjects by only analyzing noninvasive scalp EEG signals. Experimental results show that the highest classification accuracy of 97.2%, a sensitivity of 95.6%, a specificity of 98.9%, a precision of 98.9%, and the highest AUC value of 1 are achieved using SampEn feature and cubic SVM classifier (SCS Model). It is hence concluded that SampEn is an effectively distinguishing feature for classifying normal and fatigue EEG signals. The proposed system may provide us with a new and promising approach to monitoring and detecting driver fatigue at a relatively low computational cost.

EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism

“…We conducted repeated experiments, testing each feature individually, and the experimental comparisons are shown in Figure 9. To evaluate model performance, we compared our method to the RBF-TLLH method [12] and the SCS model [11] using the previously described dataset. The experiments showed that all methods were effective in feature extraction and model training when dealing with complex and diverse data.…”

“…This implied that all methods would be worthy of consideration when resources allow. Nevertheless, it is worth highlighting that 56% 60% 58% 72% 66% 64% 60% 62% To evaluate model performance, we compared our method to the RBF-TLLH method [12] and the SCS model [11] using the previously described dataset. The experiments showed that all methods were effective in feature extraction and model training when dealing with complex and diverse data.…”

“…By analyzing EEG signals using current source density (CSD) data, a directional brain network was constructed for the study of driver fatigue. Zhang [11] presented a system that utilized 14-channel EEG signals to effectively determine the fatigue influence degree in real time. This system used the sample entropy method to quantify complex EEG signals with SampEn features and a three-times support vector machine classifier (SCS model), which concluded that SampEn features could effectively distinguish between normal and fatigue EEG signals.…”