Pirondini E, Babadi B, Lamus C, Brown E N, Purdon P L
Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:6752-5. doi: 10.1109/EMBC.2012.6347544.
Cortical activity can be estimated from electroencephalogram (EEG) or magnetoencephalogram (MEG) data by solving an ill-conditioned inverse problem that is regularized using neuroanatomical, computational, and dynamic constraints. Recent methods have incorporated spatio-temporal dynamics into the inverse problem framework. In this approach, spatio-temporal interactions between neighboring sources enforce a form of spatial smoothing that enhances source localization quality. However, spatial smoothing could also occur by way of correlations within the state noise process that drives the underlying dynamic model. Estimating the spatial covariance structure of this state noise is challenging, particularly in EEG and MEG data where the number of underlying sources is far greater than the number of sensors. However, the EEG/MEG data are sparse compared to the large number of sources, and thus sparse constraints could be used to simplify the form of the state noise spatial covariance. In this work, we introduce an empirically tailored basis to represent the spatial covariance structure within the state noise processes of a cortical dynamic model for EEG source localization. We augment the method presented in Lamus, et al. (2011) to allow for sparsity enforcing priors on the covariance parameters. Simulation studies as well as analysis of real data reveal significant gains in the source localization performance over existing algorithms.
通过求解一个使用神经解剖学、计算和动态约束进行正则化的不适定逆问题，可以从脑电图（EEG）或脑磁图（MEG）数据估计皮层活动。最近的方法已将时空动力学纳入逆问题框架。在这种方法中，相邻源之间的时空相互作用强制实现一种空间平滑形式，从而提高源定位质量。然而，空间平滑也可能通过驱动基础动态模型的状态噪声过程中的相关性发生。估计这种状态噪声的空间协方差结构具有挑战性，特别是在EEG和MEG数据中，其中基础源的数量远大于传感器的数量。然而，与大量源相比，EEG/MEG数据是稀疏的，因此可以使用稀疏约束来简化状态噪声空间协方差的形式。在这项工作中，我们引入了一个根据经验定制的基来表示用于EEG源定位的皮层动态模型的状态噪声过程中的空间协方差结构。我们扩展了Lamus等人（2011年）提出的方法，以便对协方差参数施加稀疏性先验。模拟研究以及对真实数据的分析表明，与现有算法相比，源定位性能有显著提高。
