Barton Matthew J, Robinson Peter A, Kumar Suresh, Galka Andreas, Durrant-Whyte Hugh F, Guivant José, Ozaki Tohru
School of Physics, University of Sydney, Sydney, N.S.W. 2006, Australia.
IEEE Trans Biomed Eng. 2009 Jan;56(1):122-36. doi: 10.1109/TBME.2008.2006022.
Electroencephalographic (EEG) source localization is an important tool for noninvasive study of brain dynamics, due to its ability to probe neural activity more directly, with better temporal resolution than other imaging modalities. One promising technique for solving the EEG inverse problem is Kalman filtering, because it provides a natural framework for incorporating dynamic EEG generation models in source localization. Here, a recently developed inverse solution is introduced, which uses spatiotemporal Kalman filtering tuned through likelihood maximization. Standard diagnostic tests for objectively evaluating Kalman filter performance are then described and applied to inverse solutions for simulated and clinical EEG data. These tests, employed for the first time in Kalman-filter-based source localization, check the statistical properties of the innovation and validate the use of likelihood maximization for filter tuning. However, this analysis also reveals that the filter's existing space- and time-invariant process model, which contains a single fixed-frequency resonance, is unable to completely model the complex spatiotemporal dynamics of EEG data. This finding indicates that the algorithm could be improved by allowing the process model parameters to vary in space.
脑电图（EEG）源定位是用于脑动力学无创研究的重要工具，因为它能够比其他成像方式更直接地探测神经活动，且具有更好的时间分辨率。解决EEG逆问题的一种有前景的技术是卡尔曼滤波，因为它为在源定位中纳入动态EEG生成模型提供了一个自然框架。在此，引入了一种最近开发的逆解，它使用通过似然最大化调整的时空卡尔曼滤波。然后描述了用于客观评估卡尔曼滤波器性能的标准诊断测试，并将其应用于模拟和临床EEG数据的逆解。这些测试首次用于基于卡尔曼滤波的源定位，检查创新的统计特性并验证使用似然最大化进行滤波器调谐的合理性。然而，该分析还表明，滤波器现有的时空不变过程模型包含单个固定频率共振，无法完全模拟EEG数据复杂的时空动态。这一发现表明，通过允许过程模型参数在空间中变化，该算法可能会得到改进。
