Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models
Instantaneous multivariate EEG coherence analysis by means of adaptive high-dimensional autoregressive models
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DOI：
10.1016/s0165-0270(00)00350-2
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发表时间：
2001-02-15
影响因子：
3
通讯作者：
Witte, H
中科院分区：
文献类型：
--
作者：
Möller, E;Schack, B;Witte, H
本研究提出了一种有效的算法，用于将具有时间相关参数的多元自回归模型（MVAR）拟合到多维信号。因此，模型的维度可以被选择为等于信号通道的数量。利用带遗忘因子的递推最小二乘（RLS）算法估计自回归（AR）参数矩阵。估计过程包括一个单一的审判，以及合奏平均方法。后一种方法允许同时拟合一个平均MVAR模型到一组单次试验，每个试验代表相同任务的测量。这种整体平均方法的一个特别的优点是，它只需要一个低的计算工作相比，众所周知的程序应用于单一的试验。此外，集合平均方法与高适应能力相关联。使用模拟时间序列的估计的性质进行了研究。可以证明，估计的自适应能力（由其自适应速度和方差衡量）不依赖于模型维数。平均MVAR拟合适用于19维EEG数据，记录在一个基本的比较程序。讨论了普通相干和多重相干的计算。将证明多个瞬时EEG相干性的灵敏度。(C)2001爱思唯尔科技有限公司。保留所有权利。
This study presents an efficient algorithm for the fitting of multivariate autoregressive models (MVAR) with time-dependent parameters to multidimensional signals. Thereby, the dimension of the model may be chosen to equal the number of signal channels. The autoregressive (AR) parameter matrices are estimated by an extension of the recursive least squares (RLS) algorithm with forgetting factor. The estimation procedure includes a single trial as well as an ensemble mean approach. The latter approach allows the simultaneous fit of one mean MVAR model to a set of single trials, each of them representing the measurement of the same task. A particular advantage of this ensemble mean approach is that it requires only a low computation effort in comparison to well known procedures applied to single trials. Furthermore, the ensemble mean approach is linked with a high adaptation capability. The properties of the estimator are investigated using simulated time series. It can be demonstrated that the adaptation capability of the estimation (measured by its adaptation speed and variance) does not depend on the model dimension. The mean MVAR fit is applied to 19-dimensional EEG data, recorded during an elementary comparison procedure. The calculation of ordinary and multiple coherence is discussed. The sensitivity of the multiple instantaneous EEG coherence will be demonstrated. (C) 2001 Elsevier Science B.V. All rights reserved.