Dynamic visual guidance with complex task improves intracortical source activities during motor imagery.

NEUROREPORT(2019)

引用 4|浏览33
暂无评分
摘要
Motor imagery (MI) based brain-computer interfaces could be used clinically to trigger neurological recovery and improve motor function for patients with neural injuries. However, the factors that impact on MI performance and rehabilitative effect of MI-based brain-computer interfaces have not been characterized. According to our previous study, complex imagery tasks with dynamic visual paradigm could induce stronger MI features and obtain significantly higher average classification accuracy than nondynamic guidance. This study attempted to further investigate intracortical activities under different instructive paradigms and explore their potential effects on motor recovery. Eleven participants performed four types of different paradigms, including a nondynamic visual paradigm with simple MI task and three other dynamic visual/audiovisual paradigms with simple/complex MI tasks. A 64-channel electroencephalography was acquired and a voxel by voxel grand average of cortical source activities with statistical nonparametric mapping based on standardized low-resolution brain electromagnetic tomography were performed for comparisons among these paradigms in both alpha and beta bands. Moreover, seven regions of interest were selected to further analyze mean current source density variations for each paradigm with statistical analysis between dynamic and nondynamic paradigms. The outcomes uncovered that the dynamic visual aided paradigm with complex imagery tasks stimulated stronger cortical activities in core motor-related regions and triggered more extensive activation in the classical frontoparietal mirror regions than nondynamic paradigm. Involvement of these areas had a positive impact on the recovery of motor deficits in patients with neural injuries.
更多
查看译文
关键词
cortical source activity,electroencephalography,motor imagery,motor recovery,standardized low-resolution brain electromagnetic tomography
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要