Hernández J L, Biscay R, Jimenez J C, Valdes P, Grave de Peralta R
Cuban Neuroscience Center, Havana.
Int J Biomed Comput. 1995 Feb;38(2):121-9. doi: 10.1016/0020-7101(94)01044-2.
A new measure of dissimilarity between two EEG segments is proposed. It is derived from the application of the mathematical concept of distance between series of one-step predictions according to the estimated non-linear autoregressive functions. The non-linear autoregressive estimation is performed by non-parametric regression using kernel estimators. The possibility of applying this measure for automatic classification of EEG segments is explored. For this purpose multidimensional scaling and cluster analyses are applied on the basis of the calculated dissimilarity measures. In particular, its application to different EEG segments with delta activity and also with alpha waves reveals high agreement with visual classification by EEG specialists.
提出了一种用于衡量两个脑电（EEG）片段之间差异的新方法。它源自根据估计的非线性自回归函数对一步预测序列之间的距离这一数学概念的应用。非线性自回归估计通过使用核估计器的非参数回归来执行。探讨了将此方法应用于脑电片段自动分类的可能性。为此，基于计算出的差异度量进行多维缩放和聚类分析。特别是，将其应用于具有δ波活动以及α波的不同脑电片段时，发现与脑电图专家的视觉分类高度一致。
