Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm

NeuroImage

卷:225

Mithun Diwakar¹  Stefan Haufe²  Ali Hashemi³  Srikantan S. Nagarajan⁴  Kensuke Sekihara⁵  Chang Cai⁶ 

[1] Corresponding author at: National Engineering Research Center for E-Learning,Central China Normal University, Wuhan, China.;

[2] Corresponding authors.;

[3] Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States;

[4] Machine Learning Group, Electrical Engineering and Computer Science Faculty, Technische Universität Berlin, Germany;

[5] Berlin Center for Advanced Neuroimaging, Charit Universittesmedizin Berlin, Berlin, Germany;

[6] National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China;

关键词: Electromagnetic brain mapping;    Robust noise estimation;    Bayesian inference;    Inverse problem;    Magnetoencephalography;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown