Leistritz Lutz, Putsche Peter, Schwab Karin, Hesse Wolfram, Süsse Thomas, Haueisen Jens, Witte Herbert
Institute of Medical Statistics, Computer Sciences and Documentation, Medical Faculty, Friedrich Schiller University Jena, Jena, Germany.
Biomed Tech (Berl). 2007 Feb;52(1):83-9. doi: 10.1515/BMT.2007.016.
This study presents three EEG/MEG applications in which the modeling of oscillatory signal components offers complementary analysis and an improved explanation of the underlying generator structures. Coupled oscillator networks were used for modeling. Parameters of the corresponding ordinary coupled differential equation (ODE) system are identified using EEG/MEG data and the resulting solution yields the modeled signals. This model-related analysis strategy provides information about the coupling quantity and quality between signal components (example 1, neonatal EEG during quiet sleep), allows identification of the possible contribution of hidden generator structures (example 2, 600-Hz MEG oscillations in somatosensory evoked magnetic fields), and can explain complex signal characteristics such as amplitude-frequency coupling and frequency entrainment (example 3, EEG burst patterns in sedated patients).
本研究展示了三种脑电图/脑磁图（EEG/MEG）应用，其中振荡信号成分的建模提供了互补分析，并对潜在的发生器结构给出了更好的解释。采用耦合振荡器网络进行建模。利用EEG/MEG数据确定相应的常耦合微分方程（ODE）系统的参数，所得解产生建模信号。这种与模型相关的分析策略提供了关于信号成分之间耦合量和质量的信息（示例1，安静睡眠期间的新生儿脑电图），能够识别隐藏发生器结构的可能贡献（示例2，体感诱发电磁场中的600赫兹脑磁图振荡），并可以解释诸如振幅-频率耦合和频率夹带等复杂信号特征（示例3，镇静患者的脑电图爆发模式）。
