4.7 Article

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

Journal

NEUROIMAGE
Volume 225, Issue -, Pages -

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2020.117411

Keywords

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

Funding

  1. National Natural Science Foundation of China [62007013, 61772380]
  2. NIH from the NIBIB [R01EB022717, R01DC013979, R01NS100440, R01DC176960, R01DC017091, R01AG062196 UCOP-MRP-17-454755, T32EB001631]
  3. Major Project for Technological Innovation of Hubei Province [2019AAA044]
  4. Science & Technology Major Project of Hubei Province (Next-Generation AI Technologies) [2019AEA170]
  5. European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme [758985]
  6. Machine Learning/Intelligent Data Analysis research group at Technische Universitat Berlin
  7. Berlin International Graduate School in Model and Simulation based Research (BIMoS)
  8. Berlin Mathematical School (BMS)
  9. Berlin Mathematics Research Center MATH+
  10. European Research Council (ERC) [758985] Funding Source: European Research Council (ERC)

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The paper proposes several robust methods to estimate the contribution of noise from outside the brain in electromagnetic brain imaging M/EEG data, improving the reconstruction of complex brain source activity. The Champagne algorithm with noise learning shows superior performance in simulations, even without the use of any baseline data.
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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