EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed <i>via</i> a graph theoretical network

Wang, Fuwang; Zhang, Xiaolei; Fu, Rongrong; Sun, Guangbin

doi:10.1039/c8ra04846k

Cited by 31 publications

(18 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, it is necessary to effectively relieve driving fatigue. Studies have shown that repetitive and monotonous external environmental information can easily lead human beings to be in a state of mental fatigue, in which our brain activity is inhibited [50,51,52]. In our study, we tried to stimulate human brain nerves repeatedly to keep them active all the time to combat mental fatigue.…”

Section: Discussionmentioning

confidence: 99%

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Wang

2019

Sensors

View full text Add to dashboard Cite

Rapid and accurate detection of driver fatigue is of great significance to improve traffic safety. In the present work, we propose the man-machine response mode (MRM) to relieve driver fatigue caused by long-term driving. In this paper, the characteristics of the complex brain network, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional eye movement characteristics and a subjective questionnaire (SQ), the changes in driving fatigue characteristics were comprehensively analyzed. The results show that driving fatigue can be effectively delayed using the MRM. Additionally, the response equipment is low in cost and practical, so it will be practical to use in actual driving situations in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Wang

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…In this study, given that multichannel EEG signals are used, we define each electrode on the scalp surface as a network node (Step 2) We quantify the functional connectivity relationship between network nodes. One of the commonly used methods is Pearson's correlation coefficient [ 24 ]. The formula is shown as follows:

where x i ( t ) and x j ( t ) are the sampling values of nodes i and j at time t , respectively;

and

are the average sampling values of nodes i and j , respectively; and N is the number of network nodes.…”

Section: Methodsmentioning

confidence: 99%

Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network

et al. 2021

View full text Add to dashboard Cite

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.

show abstract

“…The commonly used methods are Pearson correlation coefficient, coherence spectrum, and mutual information. Pearson correlation coefficient is selected because of its better noise suppression and robustness compared with the other two methods [23]. The formula is as follows:…”

Section: Eeg Signal Featuresmentioning

confidence: 99%

“…Fast independent component analysis was used for denoising to obtain more pure EEG signals, ensuring the effectiveness of the extracted features in the subsequent work. The ERD/ERS phenomenon is related to the mu (8-12 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) rhythms of EEG signals. However, some frequencies in the beta rhythm are the harmonic waves of the mu rhythm.…”

Section: Eeg Data Preprocessingmentioning

confidence: 99%

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Luo

2020

Electronics

View full text Add to dashboard Cite

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification.

show abstract

EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network

Abstract: This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment.

Cited by 31 publications

References 48 publications

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Contact Info

Product

Resources

About