IEEE Trans Biomed Eng. 2018 Jun;65(6):1359-1372. doi: 10.1109/TBME.2017.2739824. Epub 2017 Sep 14.
Electroencephalography (EEG) and magnetoencephalography noninvasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse problem. Solutions to this problem have focused on spatial continuity constraints, dynamic modeling, or sparsity constraints. The combination of these key ideas could offer significant performance improvements, but substantial computational costs pose a challenge for practical application of such approaches. Here, we propose a new method for EEG source localization that combines: 1) covariance estimation for both source and measurement noises; 2) linear state-space dynamics; and 3) sparsity constraints, using 4) novel computationally efficient estimation algorithms.
For source covariance estimation, we use a locally smooth basis alongside sparsity enforcing priors. For EEG measurement noise covariance estimation, we use an inverse Wishart prior density. We estimate these model parameters using an expectation-maximization algorithm that employs steady-state filtering and smoothing to expedite computations.
We characterized the performance of our method by analyzing simulated data and experimental recordings of eyes-closed alpha oscillations. Our sparsity enforcing priors significantly improved estimation of both the spatial distribution and time course of simulated data, while improving computational time by more than 12-fold over previous dynamic methods.
We developed and demonstrated a novel method for improved EEG source localization employing spatial covariance estimation, dynamics, and sparsity.
Our approach provides substantial performance improvements over existing methods using computationally efficient algorithms that will facilitate practical applications in both neuroscience and medicine.
脑电图（EEG）和脑磁图（MEG）非侵入性地记录头皮电磁场，这些电磁场由脑电流产生，揭示了毫秒级别的大脑动态，这对神经科学和临床应用非常有用。估计产生这些场的电流，即源定位，是一个病态的逆问题。针对该问题的解决方案集中于空间连续性约束、动态建模或稀疏性约束。这些关键思想的结合可以带来显著的性能提升，但巨大的计算成本对这些方法的实际应用构成了挑战。在这里，我们提出了一种新的 EEG 源定位方法，该方法结合了：1）源和测量噪声的协方差估计；2）线性状态空间动态；3）稀疏性约束，使用 4）新颖的计算高效估计算法。
对于源协方差估计，我们使用局部平滑基和稀疏性增强先验。对于 EEG 测量噪声协方差估计，我们使用逆 Wishart 先验密度。我们使用期望最大化算法来估计这些模型参数，该算法采用稳态滤波和平滑技术来加速计算。
我们通过分析模拟数据和闭眼 alpha 振荡的实验记录来描述我们方法的性能。我们的稀疏性增强先验显著改善了模拟数据的空间分布和时间过程的估计，同时将计算时间提高了 12 倍以上，优于以前的动态方法。
我们开发并演示了一种用于改进 EEG 源定位的新方法，该方法采用空间协方差估计、动态和稀疏性。
我们的方法通过使用计算效率高的算法提供了对现有方法的实质性性能提升，这将促进神经科学和医学中的实际应用。
