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

• Published in: Biomedical signal processing and control 2020-09, Vol.62, p.102129, Article 102129
• Main Authors: Zou, Shuli, Qiu, Taorong, Huang, Peifan, Luo, Haowen, Bai, Xiaoming
• Format: Article
• Language: English
• Subjects: Electroencephalogram (EEG); Fatigue driving state recognition; Functional brain network (FBN); Shortest path tree
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2020.102129

Aimed at studying the method of constructing a functional brain network (FBN) that can effectively recognize the state of fatigue driving based on electroencephalogram (EEG), and analyzing which regions of the brain (electrode) are closely related to the occurrence of fatigue driving. A method based on the combination of shortest path tree (CSPT) for constructing a functional brain network (denoted as CSP-FBN) is proposed, which is applied to fatigue driving state recognition and neural mechanism analysis of fatigue driving. Through the comparison experiment of the classification accuracy in the same frequency band (beta band), the results show that the functional brain network constructed by the combined shortest path tree in fatigue state recognition is better than the functional brain network constructed by other methods, the accuracy of 10-fold cross validation reaches 99.17%. At the same time, we also find that Fz, F4, Fc3, Fcz, Fc4, C3, Cz4, Cp3, Cpz, Cp4, P3, Pz and P4 are important electrodes for fatigue driving state recognition, which reflects that the right central region and the central parietal region of the brain have a close relationship with the occurrence of fatigue driving. •A method based on the combination of shortest path tree (CSPT) for constructing a functional brain network (FBN) is proposed, which is applied to fatigue driving state recognition and neural mechanism analysis of fatigue driving.•Through the comparison experiment of the classification accuracy in the beta band, the results show that the FBN constructed by the CSPT in fatigue state recognition is better than the FBN constructed by other methods.•Fz, F4, Fc3, Fcz, Fc4, C3, Cz4, Cp3, Cpz, Cp4, P3, Pz and P4 are important electrodes for fatigue driving state recognition, which reflects that the right central region side and the central parietal region of the brain have a close relationship with the occurrence of fatigue driving.