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, in-degree, 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.
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。.
Keywords: effective networks; motor imagery; parameter attributes; small world property.