Estimation of cortical connectivity from EEG using state-space models.
Estimation of cortical connectivity from EEG using state-space models.
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DOI：
10.1109/tbme.2010.2050319
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发表时间：
2010-09
期刊：
影响因子：
--
通讯作者：
Van Veen BD
中科院分区：
文献类型：
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作者：
Cheung BL;Riedner BA;Tononi G;Van Veen BD
介绍了一种状态空间公式，用于从有噪声的头皮记录的脑电中估计皮层连通性的多变量自回归(MVAR)模型。状态方程表示大脑皮层动力学的MVAR模型，而观测方程描述大脑皮层信号与测量的脑电以及空间相关噪声的存在之间的物理联系。我们假设皮层信号来自已知的皮质区域，但每个区域内活动的空间分布是未知的。提出了一种期望最大化算法来直接估计MVAR模型参数、空间活动分布分量和噪声的空间协方差矩阵。仿真和分析表明，与两阶段方法相比，该综合方法对噪声的敏感性较低。在两阶段方法中，首先根据脑电信号估计皮层信号，然后用MVAR模型对估计的皮层信号进行拟合。通过使用被试者被动观看电影时收集的脑电数据来估计条件格兰杰因果关系，进一步证明了该方法。
A state-space formulation is introduced for estimating multivariate autoregressive (MVAR) models of cortical connectivity from noisy, scalp recorded EEG. A state equation represents the MVAR model of cortical dynamics while an observation equation describes the physics relating the cortical signals to the measured EEG and the presence of spatially correlated noise. We assume the cortical signals originate from known regions of cortex, but that the spatial distribution of activity within each region is unknown. An expectation maximization algorithm is developed to directly estimate the MVAR model parameters, the spatial activity distribution components, and the spatial covariance matrix of the noise from the measured EEG. Simulation and analysis demonstrate that this integrated approach is less sensitive to noise than two-stage approaches in which the cortical signals are first estimated from EEG measurements, and next an MVAR model is fit to the estimated cortical signals. The method is further demonstrated by estimating conditional Granger causality using EEG data collected while subjects passively watch a movie.