Cheung B L Patrick, Riedner Brady, Tononi Giulio, Van Veen Barry D
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:61-4. doi: 10.1109/IEMBS.2009.5335049.
We propose using a state-space model to estimate cortical connectivity from scalp-based EEG recordings. A state equation describes the dynamics of the cortical signals and an observation equation describes the manner in which the cortical signals contribute to the scalp measurements. The state equation is based on a multivariate autoregressive (MVAR) process model for the cortical signals. The observation equation describes the physics relating the cortical signals to the scalp EEG measurements and spatially correlated observation noise. An expectation-maximization (EM) algorithm is employed to obtain maximum-likelihood estimates of the MVAR model parameters. The strength of influence between cortical regions is then derived from the MVAR model parameters. Simulation results show that this integrated approach performs significantly better than the two-step approach in which the cortical signals are first estimated from the EEG measurements by attempting to solve the EEG inverse problem and second, an MVAR model is fit to the estimated signals. The method is also applied to data from a subject watching a movie, and confirms that feedforward connections between visual and parietal cortex are generally stronger than feedback connections.
我们建议使用状态空间模型从基于头皮的脑电图记录中估计皮质连接性。状态方程描述皮质信号的动态变化，观测方程描述皮质信号对头皮测量值产生影响的方式。状态方程基于皮质信号的多元自回归（MVAR）过程模型。观测方程描述了将皮质信号与头皮脑电图测量值以及空间相关观测噪声联系起来的物理原理。采用期望最大化（EM）算法来获得MVAR模型参数的最大似然估计。然后从MVAR模型参数中得出皮质区域之间的影响强度。仿真结果表明，这种综合方法的性能明显优于两步法，两步法是先通过尝试解决脑电图逆问题从脑电图测量值中估计皮质信号，然后将MVAR模型拟合到估计信号上。该方法还应用于一名观看电影的受试者的数据，并证实视觉皮质和顶叶皮质之间的前馈连接通常比反馈连接更强。
