Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.
Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.
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DOI：
10.1016/j.neuroimage.2020.117411
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发表时间：
2021-01-15
期刊：
影响因子：
5.7
通讯作者：
Nagarajan SS
中科院分区：
文献类型：
--
作者：
Cai C;Hashemi A;Diwakar M;Haufe S;Sekihara K;Nagarajan SS
对多个相关脑源的数量、位置和活动的鲁棒估计长期以来一直是来自M/EEG数据的电磁脑成像中的具有挑战性的任务，该任务受到来自自发脑活动、传感器噪声和其他伪影源的干扰的显著影响。最近，我们介绍了香槟算法，一种新的贝叶斯推理算法，已显示出巨大的成功M/EEG源重建。香槟和大多数其他相关贝叶斯重建算法的固有假设是，传感器数据中的噪声协方差可以从“基线”或“控制”测量中估计。然而，在许多情况下，这样的基线数据是不可用的，或者是不可靠的，并且不清楚如何最好地估计噪声协方差。在本技术说明中，我们提出了几种稳健的方法来估计来自大脑外部的噪声对传感器的贡献，而无需额外的基线测量。这些对角噪声协方差估计方法的结合提高了在高水平的噪声和干扰下复杂脑源活动的鲁棒重建，同时保持了香槟的性能特征。具体而言，我们表明，由此产生的算法，香槟与噪声学习，是相当强大的初始化，是计算效率。在仿真中，所提出的噪声学习算法的性能始终优于上级的香槟没有噪声学习。我们还证明，即使不使用任何基线数据，具有噪声学习的香槟也能够通过几次试验甚至一次试验重建复杂的大脑活动，这表明电磁脑成像源重建的显着改进。
Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from “baseline” or “control” measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for diagonal noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging.
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