Biscay R, Lavielle M, González A, Clark I, Valdés P
Cuban Neuroscience Center, Havana.
Int J Biomed Comput. 1995 Feb;38(2):189-96. doi: 10.1016/0020-7101(94)01052-3.
A new approach for EEG segmentation is introduced. This is based on a methodology for optimal segmentation of non-stationary signals derived from the maximum a posteriori estimation principle. It is a model-based, not sequential approach that allows for segmentation at different resolution levels. The features of the methodology are illustrated by its application to EEG recordings containing several types of spectral changes due to normal and pathological variations of spontaneous brain rhythmic activities, as well as physiological artifacts.
本文介绍了一种用于脑电图（EEG）分割的新方法。该方法基于一种从最大后验估计原理推导出来的非平稳信号最优分割方法。它是一种基于模型的方法，而非顺序性方法，允许在不同分辨率水平上进行分割。通过将该方法应用于包含由于自发脑节律活动的正常和病理变化以及生理伪迹而产生的几种频谱变化类型的脑电图记录，展示了该方法的特点。
