用一种新的时变自回归（TVAR）方法估计事件相关同步变化。
Estimation of event-related synchronization changes by a new TVAR method.
作者信息
Kaipio J P, Karjalainen P A
机构信息
Department of Applied Physics, University of Kuopio, Finland.
出版信息
IEEE Trans Biomed Eng. 1997 Aug;44(8):649-56. doi: 10.1109/10.605421.
The modeling of nonstationary electroencephalogram (EEG) with time-varying autoregressive (TVAR) models is discussed. The classical least squares TVAR approach is modified so that prior assumptions about the signal can be taken into account in an optimal way. The method is then applied to the estimation of event-related synchronization changes in the EEG. The results show that the new approach enables effective estimation of the parameter evolution of the time-varying EEG with better time resolution compared to previous methods. The new method also allows single-trial analysis of the event-related synchronization.
讨论了使用时变自回归（TVAR）模型对非平稳脑电图（EEG）进行建模。对经典的最小二乘TVAR方法进行了修改，以便能够以最优方式考虑关于信号的先验假设。然后将该方法应用于脑电图中事件相关同步变化的估计。结果表明，与先前方法相比，新方法能够以更好的时间分辨率有效地估计时变脑电图的参数演变。新方法还允许对事件相关同步进行单次试验分析。
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