Jimenez J C, Biscay R, Montoto O
Centro Nacional de Investigaciones Cientificas, Ciudad de la Habana, Cuba.
Biol Cybern. 1995;72(3):249-59. doi: 10.1007/BF00201488.
A spatial-temporal model for the description of electroencephalographic (EEG) data is introduced that combines smooth reconstruction in the spatial domain and autoregressive representation in the time domain. Its spatial aspect is formulated in a general framework that covers interpolation, smoothing, and regression. Contrary to the multivariate time series models used for EEG analysis up to date, the introduced model provides a smooth spatial reconstruction of the EEG cross-spectrum, keeping the condition of nonnegative definiteness. As an instance of practical importance, the case in which the spatial reconstruction is based on spherical splines is developed in detail. Illustrative examples are presented that show the flexibility of the model to describe both normal and abnormal EEG data.
介绍了一种用于描述脑电图（EEG）数据的时空模型，该模型结合了空间域中的平滑重建和时间域中的自回归表示。其空间方面是在一个涵盖插值、平滑和回归的通用框架中制定的。与迄今为止用于EEG分析的多元时间序列模型不同，所引入的模型提供了EEG交叉谱的平滑空间重建，同时保持非负定性条件。作为一个具有实际重要性的实例，详细阐述了基于球面样条进行空间重建的情况。给出了说明性示例，展示了该模型描述正常和异常EEG数据的灵活性。
