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.
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Feb 25;37(1):38-44. doi: 10.7507/1001-5515.201811013.
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 ( = 0.007) and ROI3 ( = 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.
基于脑网络的脑功能机制与认知状态研究具有至关重要的意义。本文依据一种用于从头皮记录的脑电图（EEG）信号中测量随时间和频率的定向相互作用的时频方法——偏定向相干（PDC），提出了动态PDC（dPDC）方法来构建运动想象的脑网络模型。基于2008年脑机接口竞赛IV的数据集，计算了9名受试者有效网络的参数属性（出度、入度、聚类系数和偏心度），进而分析了网络特征不同位置之间的相互作用以及运动想象的显著性。两组的聚类系数均高于随机网络，且路径长度接近随机网络。这些实验结果表明有效网络具有小世界特性。对左右手的网络参数属性分析证实，在出度方面，ROI2（p = 0.007）和ROI3（p = 0.002）区域存在显著差异。基于dPDC算法的有效网络在不同脑区之间的信息流表明，运动想象的活跃区域主要位于额中央区域（ROI2和ROI3）以及顶枕区域（ROI5和ROI6）。因此，基于dPDC算法的有效网络能够有效地反映想象运动的变化，并可作为研究神经机制的实用指标。
