The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.

The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.

In the present work, we propose the multi-acupoint electrical stimulation (stimulating the Láogóng point (劳宫PC8) and acupuncture points on waist, shoulders, buttocks of the human body) combined with music conditioning (MESCMC) to alleviate driving fatigue. In our study, the complexity of α and β rhythms of EEG of the drivers, the relative power spectrum of θ and β, as well as two relative power spectrum ratio θ/β and (θ+α)/(α+β) are used as fatigue features during driving. The features of the complexity, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional relative power spectrum features, the changes in driving fatigue features were comprehensively analyzed. The results show that the MESCMC method can effectively alleviate the mental fatigue of drivers. Besides, compared with the single-acupoint electrical stimulation[only stimulating the Láogóng point (劳宫PC8)] (SES) method, the MESCMC method is more effective in relieving driving fatigue.The mitigation equipment is low in cost and practical, and the MESCMC method is individualized and improves the universality of driving fatigue detection and relieve, so will be practical to use in actual driving situations in the future.

No abstract

In long-term continuous driving, driving fatigue is the main cause of traffic accidents. Therefore, accurate and rapid detection of driver mental fatigue is of great significance to traffic safety. In our study, the electroencephalogram (EEG) signals of subjects were preprocessed to remove interference signals. The Butterworth band-pass filter is used to extract the EEG signals of α and β rhythms, and then the basic scale entropy of α and β rhythms is used as driving fatigue characteristics. In addition, combined with the fast multiple autoregressive (MVAR) model and phase slope index (PSI), short-term data is used to accurately estimate the effective connectivity of EEG signals between different channels, and analyzed the causality flow direction in the left and right prefrontal regions of drivers at different driving stages. Further comprehensive analysis of the driver’s driving fatigue state in the continuous driving phase. Finally, the correlation coefficient value between the parameter pairs (basic scale entropy, clustering coefficient, global efficiency) is calculated. The results showed that the causality flow outflow degree of prefrontal lobe decreased during the transition from sober driving state to tired driving state. The left and right prefrontal lobes were the source of causality in sober driving state, and gradually became the target of causality with the occurrence of driving fatigue. The results showed that when transitioning from a waking state to a fatigued driving state, the causal flow direction out-degree value of the prefrontal cortex on a declining curve, and the left and right prefrontal cortex exhibited the causal source in the awake driving state, which gradually changed into the causal target along with the occurrence of driving fatigue. The three parameters of basic scale entropy, clustering coefficient and global efficiency are used as driving fatigue characteristics, and every two parameters have strong correlation. It shows that the combination of basic scale entropy and MVAR-PSI method can effectively detect the driver’s long-term driving fatigue state in continuous driving mode.