One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain

• Published in: International journal of neural systems 2023-05, Vol.33 (6)
• Main Authors: Xu, Fangzhou, Wang, Chongfeng, Yu, Xin, Zhao, Jinzhao, Liu, Ming, Zhao, Jiaqi, Gao, Licai, Jiang, Xiuquan, Zhu, Zhaoxin, Wu, Yongjian, Wang, Dezheng, Feng, Shanxin, Yin, Sen, Zhang, Yang, Leng, Jiancai
• Format: Article
• Language: English
• Subjects: Biomarkers; Cerebral cortex; Clinical significance; Electroencephalography; Frequencies; Human-computer interface; Occipital lobes; Pain; Plastic properties; Rehabilitation; Spinal cord injuries
• ISSN: 0129-0657; 1793-6462
• DOI: 10.1142/S0129065723500302

Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the β and γ frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients’ frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients’ MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain–computer interface of CNP patients.