Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich-Schiller-University Jena,07740 Jena, Germany.
IEEE Trans Biomed Eng. 2011 Feb;58(2):332-8. doi: 10.1109/TBME.2010.2063028. Epub 2010 Aug 3.
Synchronization analysis of multitrial EEG or (magneto encephalogram) MEG signals is an excellent approach to detect functional connectivity between different neuronal oscillators. In our current research, the n:m phase synchronization index (n:m PSI ) is of special interest. We prove the existence of stable and unstable synchronies dependent upon the analysis frequencies and show that they lie closely together in the frequency domain. Thus, a plot of the time-frequency plane of the n:m PSI automatically violates the sampling theorem and accordingly, the method cannot be considered as a black box. A frequency-tiling approach is presented that can detect robust synchronies while ignoring the unstable ones. The improved synchrony detection is evaluated in numerical experiments on using both simulated and real-life data. It can be demonstrated that the transient synchronization events between MEG oscillations in distant frequency ranges can be detected and that compactly textured EEG synchronization patterns can be reliably characterized.
多试 EEG 或(脑磁图)MEG 信号的同步分析是检测不同神经元振荡器之间功能连接的一种极好方法。在我们当前的研究中,n:m 相位同步指数(n:m PSI)特别有趣。我们证明了稳定和不稳定同步的存在取决于分析频率,并表明它们在频域中紧密结合。因此,n:m PSI 的时频平面的绘制自动违反了采样定理,因此,该方法不能被视为黑盒。提出了一种频率平铺方法,该方法可以在忽略不稳定同步的情况下检测稳健的同步。改进的同步检测在使用模拟和实际数据的数值实验中进行了评估。可以证明,可以检测到遥远频率范围内 MEG 振荡之间的瞬态同步事件,并且可以可靠地描述紧凑纹理的 EEG 同步模式。