The functional brain network based on the combination of shortest path tree and its application in fatigue driving state recognition and analysis of the neural mechanism of fatigue driving

2020 ◽  
Vol 62 ◽  
pp. 102129 ◽  
Author(s):  
Shuli Zou ◽  
Taorong Qiu ◽  
Peifan Huang ◽  
Haowen Luo ◽  
Xiaoming Bai
Keyword(s):  
Shortest Path ◽  
Neural Mechanism ◽  
Brain Network ◽  
Shortest Path Tree ◽  
State Recognition ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Fatigue Driving

scholarly journals Construction and Application of Functional Brain Network Based on Entropy

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1234
Author(s):  
Lingyun Zhang ◽  
Taorong Qiu ◽  
Zhiqiang Lin ◽  
Shuli Zou ◽  
Xiaoming Bai
Keyword(s):  
Dynamic Properties ◽  
Recognition Rate ◽  
Brain Network ◽  
Eeg Signals ◽  
Data Set ◽  
Functional Brain ◽  
Model Based ◽  
Functional Brain Network ◽  
Fatigue Driving ◽  
The Brain

Functional brain network (FBN) is an intuitive expression of the dynamic neural activity interaction between different neurons, neuron clusters, or cerebral cortex regions. It can characterize the brain network topology and dynamic properties. The method of building an FBN to characterize the features of the brain network accurately and effectively is a challenging subject. Entropy can effectively describe the complexity, non-linearity, and uncertainty of electroencephalogram (EEG) signals. As a relatively new research direction, the research of the FBN construction method based on EEG data of fatigue driving has broad prospects. Therefore, it is of great significance to study the entropy-based FBN construction. We focus on selecting appropriate entropy features to characterize EEG signals and construct an FBN. On the real data set of fatigue driving, FBN models based on different entropies are constructed to identify the state of fatigue driving. Through analyzing network measurement indicators, the experiment shows that the FBN model based on fuzzy entropy can achieve excellent classification recognition rate and good classification stability. In addition, when compared with the other model based on the same data set, our model could obtain a higher accuracy and more stable classification results even if the length of the intercepted EEG signal is different.

Functional brain network reorganization in HIV infection

2021 ◽  
Author(s):  
Silvia Minosse ◽  
Eliseo Picchi ◽  
Francesca Di Giuliano ◽  
Loredana Sarmati ◽  
Elisabetta Teti ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Brain Network Reorganization

A common functional brain network for autobiographical, episodic, and semantic memory retrieval

NeuroImage ◽  
2010 ◽  
Vol 49 (1) ◽  
pp. 865-874 ◽  
Author(s):  
Hana Burianova ◽  
Anthony R. McIntosh ◽  
Cheryl L. Grady
Keyword(s):  
Semantic Memory ◽  
Memory Retrieval ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network

Traumatic brain injury alters the functional brain network mediating working memory

Brain Injury ◽  
2011 ◽  
Vol 25 (12) ◽  
pp. 1170-1187 ◽  
Author(s):  
Maki Kasahara ◽  
David K. Menon ◽  
Claire H. Salmond ◽  
Joanne G. Outtrim ◽  
Joana V. Taylor Tavares ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Working Memory ◽  
Brain Injury ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network
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