Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
PLoS One. 2012;7(6):e38292. doi: 10.1371/journal.pone.0038292. Epub 2012 Jun 25.
The amplitude and latency of single-trial EEG/MEG signals may provide valuable information concerning human brain functioning. In this article we propose a new method to reliably estimate single-trial amplitude and latency of EEG/MEG signals. The advantages of the method are fourfold. First, no a-priori specified template function is required. Second, the method allows for multiple signals that may vary independently in amplitude and/or latency. Third, the method is less sensitive to noise as it models data with a parsimonious set of basis functions. Finally, the method is very fast since it is based on an iterative linear least squares algorithm. A simulation study shows that the method yields reliable estimates under different levels of latency variation and signal-to-noise ratioÕs. Furthermore, it shows that the existence of multiple signals can be correctly determined. An application to empirical data from a choice reaction time study indicates that the method describes these data accurately.
单次 EEG/MEG 信号的幅度和潜伏期可能提供有关人脑功能的有价值信息。在本文中，我们提出了一种新的方法来可靠地估计 EEG/MEG 信号的单次幅度和潜伏期。该方法具有四个优点。首先，不需要先验指定的模板函数。其次，该方法允许多个信号在幅度和/或潜伏期上独立变化。第三，该方法对噪声的敏感性较低，因为它使用一组简洁的基函数对数据进行建模。最后，该方法非常快，因为它基于迭代线性最小二乘算法。一项模拟研究表明，该方法在不同的潜伏期变化和信噪比水平下都能得到可靠的估计。此外，它表明可以正确确定多个信号的存在。对来自选择反应时研究的经验数据的应用表明，该方法可以准确地描述这些数据。
