Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data
Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data
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DOI：
10.1016/j.neuroimage.2011.12.027
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发表时间：
2012-03-01
期刊：
影响因子：
5.7
通讯作者：
Nagarajan, Srikantan S.
中科院分区：
文献类型：
--
作者：
Owen, Julia P.;Wipf, David P.;Nagarajan, Srikantan S.
在本文中，我们提出了一种新的源定位算法，香槟广泛的性能评估。它是在一个经验贝叶斯框架，产生稀疏的解决方案的反问题。它对相关源具有鲁棒性，并学习非刺激诱发活动的统计数据，以抑制噪声和干扰大脑活动的影响。我们在模拟和真实的M/EEG数据上测试了香槟。选择用于模拟数据的源位置以测试具有挑战性的源配置的性能。在模拟中，我们发现，香槟优于基准算法在源定位的准确性和源时间过程的正确估计。我们还证明，香槟是更强大的相关脑活动存在于真实的MEG数据，并能够解决许多不同的和功能相关的脑区与真实的MEG和EEG数据。(C)2011 Elsevier Inc. All rights reserved.
In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data. (C) 2011 Elsevier Inc. All rights reserved.