Biomagnetic Imaging Laboratory, Dept. Radiology and Biomedical Imaging, UCSF San Francisco, CA, USA.
Neuroimage. 2012 Mar;60(1):305-23. doi: 10.1016/j.neuroimage.2011.12.027. Epub 2011 Dec 23.
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.
在本文中，我们对一种新颖的源定位算法 Champagne 进行了全面的性能评估。该算法是在经验贝叶斯框架中推导出来的，它对逆问题产生稀疏解。它对相关源具有鲁棒性，并学习非刺激诱发活动的统计信息，以抑制噪声和干扰脑活动的影响。我们在模拟和真实 M/EEG 数据上对 Champagne 进行了测试。模拟数据中使用的源位置是为了测试在具有挑战性的源配置下的性能而选择的。在模拟中，我们发现 Champagne 在源定位的准确性和源时间过程的正确估计方面都优于基准算法。我们还证明，Champagne 对真实 MEG 数据中存在的相关脑活动更具有鲁棒性，并且能够用真实的 MEG 和 EEG 数据解析许多不同的和功能相关的脑区。
