Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging

Cai, Chang; Diwakar, Mithun; Chen, Dan; Sekihara, Kensuke; Hinkley, Leighton B.

doi:10.1109/tmi.2019.2932290

Cited by 28 publications

(32 citation statements)

References 39 publications

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“…Although the algorithms presented in Sections III-A1-III-A3 achieve satisfactory performance in terms of computational complexity, their reconstruction performance degrades significantly in low-SNR regimes. This behavior has been theoretically shown in [22, Section VI-E] and has also been confirmed in several simulation studies [23], [24].…”

Section: Lowsnr-brain Source Imaging (Lowsnr-bsi)supporting

confidence: 82%

“…This holds in particular for the reconstruction of ongoing as well as induced (non-phase-locked) oscillatory activity, where no averaging can be performed prior to source reconstruction. Current SBL algorithms may suffer from reduced performance in such low-SNR regimes [22]- [24]. To overcome this limitation, we propose a novel MM algorithm for EEG/MEG source imaging, which employs a bound on the SBL cost function that is particularly suitable for low-SNR regimes.…”

Section: Introductionmentioning

confidence: 99%

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Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

Hashemi

Cai

Kutyniok

et al. 2020

Preprint

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Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of SBL in three important ways. First, we reformulate three existing SBL algorithms under the majorization-minimization (MM) framework. This unification perspective not only provides a useful theoretical framework for comparing different algorithms in terms of their convergence behavior, but also provides a principled recipe for constructing novel algorithms with specific properties by designing appropriate bounds of the Bayesian marginal likelihood function. Second, building on the MM principle, we propose a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio settings. Third, precise knowledge of the noise level is a crucial requirement for accurate source reconstruction. Here we present a novel principled technique to accurately learn the noise variance from the data either jointly within the source reconstruction procedure or using one of two proposed cross-validation strategies. Empirically, we could show that the monotonous convergence behavior predicted from MM theory is confirmed in numerical experiments. Using simulations, we further demonstrate the advantage of LowSNR-BSI over conventional SBL in low-SNR regimes, and the advantage of learned noise levels over estimates derived from baseline data. To demonstrate the usefulness of our novel approach we show neurophysiologically plausible source reconstructions on averaged auditory evoked potential data.

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Section: Lowsnr-brain Source Imaging (Lowsnr-bsi)supporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

Hashemi

Cai

Kutyniok

et al. 2020

Preprint

Self Cite

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“…To evaluate the performance of localization results, we use free-response receiver operator characteristics (FROC) which shows the probability for detection of a true source in an image versus the expected value of the number of false positive detections per image [4,17,15,18]. Based on the FROC, we compute an A metric [19,20] which is an estimate of the area under the FROC curve for each simulation. If the area under the FROC curve is large, then the hit rate is higher compared to the false positive rate.…”

Section: Quantifying Algorithm Performancementioning

confidence: 99%

“…The false rate (F R ) is defined by dividing the number of potential false positive voxels by the total number of false voxels for each simulation. The details of the A metric calculation can be referred to in our previous paper [20]. We then calculate the correlation coefficient between the seed and estimated source time courses for each hit, which is used to determine the accuracy of the time course reconstructions and denoted as R .…”

Section: Quantifying Algorithm Performancementioning

confidence: 99%

“…We then calculate the correlation coefficient between the seed and estimated source time courses for each hit, which is used to determine the accuracy of the time course reconstructions and denoted as R . Finally, we combine these two metrics that capture both the accuracy of the location and time courses of the algorithms into a single metric called the Aggregate Performance (AP) [4,15,20]:…”

Section: Quantifying Algorithm Performancementioning

confidence: 99%

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Robust Estimation of Noise for Electromagnetic Brain Imaging with the Champagne Algorithm

Cai¹,

Diwakar²,

Hashemi

et al. 2020

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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 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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Brain Source Reconstruction Solution Quality Assessment with Spatial Graph Frequency Features

et al. 2022

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Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging

Cited by 28 publications

References 39 publications

Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

Robust Estimation of Noise for Electromagnetic Brain Imaging with the Champagne Algorithm

Brain Source Reconstruction Solution Quality Assessment with Spatial Graph Frequency Features

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