基于fMRI功能网络和贝叶斯矩阵分解的脑电源成像方法

脑电(EEG)是一种重要的脑功能成像技术，根据头皮记录的EEG信号重构皮层脑活动称为EEG源成像。然而脑源活动位置和尺寸的准确重构依然是一个挑战。为充分利用EEG和功能磁共振(fMRI)信号在时空分辨率上的互补信息，该文提出一个新的源成像方法——基于fMRI脑网络和时空约束的EEG源重构算法(FN-STCSI)。该方法在参数贝叶斯框架下，基于矩阵分解思想将源信号分解为若干时间基函数的线性组合。此外，为融合fMRI的高空间分辨率信息，FN-STCSI利用独立成分分析提取fMRI信号的功能网络，构建EEG源成像的空间协方差基，通过变分贝叶斯推断技术确定每个空间协方差基的相对贡献，实现EEG-fMRI融合。通过蒙特卡罗数值仿真和实验数据分析比较了FN-STCSI与现有算法在不同信噪比和不同先验条件下的性能，结果表明FN-STCSI能有效融合EEG-fMRI在时空上的互补信息，提高EEG弥散源成像的性能。

EEG Source Imaging Based on fMRI Functional Network and Bayesian Matrix Decomposition

ElectroEncephaloGraphy (EEG) is an important brain functional imaging technology. The task to reconstruct cortical activities based on the scalp EEG is called EEG source imaging. However, the accurate reconstruction of the locations and sizes of brain source activity remains a challenge. To employ fully the spatiotemporal complementary information of EEG and functional Magnetic Resonance Imaging (fMRI), a new EEG source imaging algorithm, i.e., FN-STCSI (Functional Network based Spatio-Temporal Constrains Source Imaging) is proposed. Specifically, to make full use of the temporal information of EEG signals, the source signal matrix is decomposed into a linear combination of several time basis functions based on the idea of matrix decomposition. Additionally, to fuse the high spatial resolution information of fMRI, FN-STCSI employes independent component analysis to extract the fMRI functional networks. Then these fMRI networks are used to construct the spatial covariance basis for EEG source imaging. Variational Bayesian inference techniques are used to determine the relative contribution of each spatial covariance basis to realize EEG-fMRI fusion. Through Monte Carlo numerical simulation and experimental data analysis, FN-STCSI is compared with existing algorithms under different signal-to-noise ratios and different prior conditions. The results show that FN-STCSI can effectively fuse the complementary spatiotemporal information of EEG-fMRI and improve the performance of EEG extended source imaging.