基于动态偏定向相干的运动想象因效性网络分析  

Analysis of imagery motor effective networks based on dynamic partial directed coherence

作　　者：李亚兵[1,2] 谢松云 于圳宁[3] 谢辛舟 段绪 刘畅[1] LI Yabing;XIE Songyun;YU Zhenning;XIE Xinzhou;DUAN Xu;LIU Chang(School of Electronics and Information,Northwestern Polytechnical University,Xi’an 710129,P.R.China;School of Computer Science and Technology,Xi’an University of Posts&Telecommunications,Xi’an 710121,P.R.China;Beijing Institute of Computer Application Technology,Beijing 100089,P.R.China)

机构地区：[1]西北工业大学电子信息学院,西安710129 [2]西安邮电大学计算机学院,西安710121 [3]中国兵器工业计算机应用技术研究所,北京100089

出　　处：《生物医学工程学杂志》2020年第1期38-44,共7页Journal of Biomedical Engineering

基　　金：国家自然科学基金（61273250）;中德联合脑机交互与脑控技术国际联合研究中心（3102017jc11002）;陕西省重点研发计划（2018ZDXM-GY-101）

摘　　要：利用脑网络对脑功能机制和脑认知状态进行基础研究具有重要的意义。本文依据一种测量头皮脑电信号(EEG)的时间-频率域相互作用的方法,即偏定向相干(PDC),提出了动态PDC(dPDC)算法对运动想象的因效性网络建模。研究利用2008年第四届BCI竞赛数据的9个被试计算了不同运动想象任务下因效性网络的参数特征(出入度、集群系数、离心率等),通过显著性检验分析了左、右手运动想象在不同脑区EEG信号的交互影响。结果表明,左右手想象任务的网络集群系数大于随机网络,且特征路径长度与随机网络近似,验证了该网络的小世界特性。对左、右手运动想象的网络特征参数的分析对比,验证了两种任务部分特征具有显著差异,如:针对出度的统计分析表明,在ROI2(P=0.007)和ROI3(P=0.002)区域具有显著差异。基于dPDC算法的因效性网络对运动想象脑区间信息流变化的分析表明,左、右手运动想象的活动区域主要位于左右侧中央前回(ROI2和ROI3)和左右侧中央枕区(ROI5和ROI6)。因此,基于dPDC的因效性网络可以有效表征运动想象的状态,为研究提供了新的手段。The research on brain functional mechanism and cognitive status based on brain network has the vital significance.According to a time–frequency method,partial directed coherence(PDC),for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram(EEG)signals,this paper proposed dynamic PDC(dPDC)method to model the brain network for motor imagery.The parameters attributes(out-degree,indegree,clustering coefficient and eccentricity)of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008,and then the interaction between different locations for the network character and significance of motor imagery was analyzed.The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network.These experimental results show that the effective network has a small world property.The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2(P=0.007)and ROI3(P=0.002)regions for out-degree.The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions(ROI2 and ROI3)and parieto-occipital regions(ROI5 and ROI6).Therefore,the effective network based dPDC algorithm can be effective to reflect the change of imagery motor,and can be used as a practical index to research neural mechanisms.

关 键 词：因效性网络 运动想象 参数特征 小世界特性 

分 类 号：TN911.7[电子电信—通信与信息系统] R338[电子电信—信息与通信工程]