Real-Time EEG-Based Detection of Fatigue Driving Danger for Accident Prediction

Abstract

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of driver fatigue was analyzed. The functional brain networks were employed to track the fatigue impact on processing capacity of brain. The results show the overall functional connectivity of the subjects is weakened after long time driving tasks. The regularity is summarized as the fatigue convergence phenomenon. Based on the fatigue convergence phenomenon, we combined both the input and global synchronizations of brain together to calculate the residual amount of the information processing capacity of brain to obtain the dangerous points in real time. Finally, the danger detection system of the driver fatigue based on the neural mechanism was validated using accident EEG. The time distributions of the output danger points of the system have a good agreement with those of the real accident points.

Authentication of Brain-Computer Interface Users in Network Applications

“…Although currently BCIs are an open technology with multitude of applications (e.g. entertainment [35][36], assessment of cognitive workload [37], etc. ), originally they were intended for people with intact cognitive and intellectual capacities but without the ability to move their muscles.…”

A novel time-frequency multilayer network for multivariate time series analysis

“…Nowadays, electroencephalogram (EEG) signals have been successfully used to quantitatively assess the driver mental state. Wang et al [47] employed wavelet entropy and pulse coupled neural network to detect potential danger during fatigue driving from EEG signals. Hajinoroozi et al [48] introduced the convolutional neural network to predict the driver's cognitive states from EEG signals.…”

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

“…Although these methods have been experimentally demonstrated the effectiveness in various fields, due to the requirement of data after change point, a large detection delay is an essential limitation of these methods for real applications [13]. On the other hand, real-time change detection aims to detect changes as soon as possible when a change occurs, this requirement is crucial in many real-life scenarios such as security monitoring [14,15], health care [16,17], automated factory [18,19] as well as machine operation monitoring studied in this paper. In operation of real-time change detection, at each time when a datum is input, it evaluates what extent the input datum is likely to be a change-point by a certain type of measuring score [20] which does not need any input data after change time.…”