Tensor-based dynamic brain functional network for motor imagery classification

• Published in: Biomedical signal processing and control Vol. 69; p. 102940
• Main Authors: Zhang, Qizhong, Guo, Bin, Kong, Wanzeng, Xi, Xugang, Zhou, Yizhi, Gao, Farong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2021
• Subjects: Brain-computer interface; Dynamic brain functional network; Motor imagery; Tensor decomposition; Brain-computer interface; Tensor decomposition; Dynamic brain functional network; Motor imagery
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102940

•A tensor model of the dynamic brain functional network is proposed.•This method relies on the changing of the interaction of various brain regions.•Orthogonal decomposition of partially symmetric tensors can extract MI features.•The identification electrode is located near Cz during the MI period. The classification of motor imagery (MI) task based on Electroencephalography (EEG) is an important problem in brain-computer interface (BCI) system. The high-precision classification of MI is a challenging task in which the process of feature extraction is crucial step. In this work, we propose a tensor model of a dynamic brain functional network (DBFN) to decode motion intentions. First, we construct the brain functional network in each small window. Then, the BFN of each time window is superimposed into a DBFN tensor with time as the axis. A tensor decomposition method with orthogonal and partial symmetric constraints is used to analyze the DBFN. Finally, the core tensor features are used as an input of the extreme learning machine (ELM) for classification. The results show that the proposed method is better than the degree, clustering coefficient of network, and principal component analysis of DBFN matrix model and the average accuracies are improved by 17.33%, 12.91%, and 17.5% under ELM, respectively. Moreover, the classification accuracy of the proposed method has the lowest variance, i.e., 5.96, indicating that the core tensor features are more adaptable to the subjects. The proposed method has the highest accuracy of 95% under both ELM and support vector machine (SVM). The average accuracy rates of ELM and SVM are 87.08% and 85.83%, respectively. The proposed method effectively extracts the EEG signal characteristics of MI and has strong robustness. This provides a reference for further research on the feature extraction algorithm of BCI.