Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury

Han Li; Ming Liu; Xin Yu; JianQun Zhu; Chongfeng Wang; Xinyi Chen; Chao Feng; Jiancai Leng; Yang Zhang; Fangzhou Xu

doi:10.3389/fnins.2022.1097660

Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury

Front Neurosci. 2023 Jan 13:16:1097660. doi: 10.3389/fnins.2022.1097660. eCollection 2022.

Authors

Han Li¹, Ming Liu¹, Xin Yu¹, JianQun Zhu¹, Chongfeng Wang¹, Xinyi Chen¹, Chao Feng¹, Jiancai Leng¹, Yang Zhang², Fangzhou Xu¹

Affiliations

¹ International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China.
² Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China.

Abstract

Background: Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients.

Methods: According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.

Results: The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.

Conclusion: The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.

Keywords: brain-computer interface; coherence-based graph convolutional network; electroencephalogram; motor imagery; spinal cord injury.

Grants and funding

This research was funded by the Introduce Innovative Teams of 2021 “New High School 20 Items” Project, Grant No. 2021GXRC071, the Program for Youth Innovative Research Team in the University of Shandong Province in China, Grant No. 2019KJN010, the Natural Science Foundation of China, Grant Nos. 82172535 and 62271293, the Natural Science Foundation of Shandong Province, Grant Nos. ZR2019MA037, ZR2022MF289, and ZR202102200383, the Research Leader Program of Jinan Science and Technology Bureau, Grant No. 2019GXRC061, the Graduate Education and Teaching Reform Research Project of Qilu University of Technology in 2019, Grant No. YJG19007, the Graduate Education and Degree Site Construction and Development Projects of Qilu University of Technology in 2022, the Talent Training and Teaching Reform Project of Qilu University of Technology in 2022 under Grant No. P202204, the Fundamental Research Funds for the Central Universities under Grant No. 2022JC013, and the School-level Teaching and Research Projects of Qilu University of Technology in 2021 under Grant No. 2021yb08.