EEG decoding method based on multi-feature information fusion for spinal cord injury. (December 2022)

• Record Type: Journal Article
• Authors: Xu, Fangzhou; Li, Jincheng; Dong, Gege; Li, Jianfei; Chen, Xinyi; Zhu, Jianqun; Hu, Jinglu; Zhang, Yang; Yue, Shouwei; Wen, Dong; Leng, Jiancai
• Is Part Of: Neural networks. Volume 156(2022)
• Page Start: 135
• Page End: 151
• Publication Date: 2022-12
• Subjects: Brain–computer interface -- Electroencephalography -- Motor imagery -- Modified graph convolution neural network -- Spinal cord injury; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.09.016
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: To develop an efficient brain–computer interface (BCI) system, electroencephalography (EEG) measures neuronal activities in different brain regions through electrodes. Many EEG-based motor imagery (MI) studies do not make full use of brain network topology. In this paper, a deep learning framework based on a modified graph convolution neural network (M-GCN) is proposed, in which temporal-frequency processing is performed on the data through modified S-transform (MST) to improve the decoding performance of original EEG signals in different types of MI recognition. MST can be matched with the spatial position relationship of the electrodes. This method fusions multiple features in the temporal-frequency-spatial domain to further improve the recognition performance. By detecting the brain function characteristics of each specific rhythm, EEG generated by imaginary movement can be effectively analyzed to obtain the subjects' intention. Finally, the EEG signals of patients with spinal cord injury (SCI) are used to establish a correlation matrix containing EEG channel information, the M-GCN is employed to decode relation features. The proposed M-GCN framework has better performance than other existing methods. The accuracy of classifying and identifying MI tasks through the M-GCN method can reach 87.456%. After 10-fold cross-validation, the average accuracy rate is 87.442%, which verifies the reliability and stability of the proposed algorithm. Furthermore, the method provides effective rehabilitation training for patients with SCI to partially restore motor function.