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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 (SNR) 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., (Copyright © 2021. Published by Elsevier Inc.)

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.

Objective: Large-amplitude artifacts from vagus nerve stimulator (VNS) implants for refractory epilepsy affect magnetoencephalography (MEG) recordings and are difficult to reject, resulting in unusable data from this important population of patients who are frequently evaluated for surgical treatment of epilepsy. Here we compare the performance of two artifact removal algorithms for MEG data: dual signal subspace projection (DSSP) and temporally extended signal space separation (tSSS)., Approach: Each algorithm's performance was first evaluated in simulations. We then tested the performance of each algorithm on resting-state MEG data from patients with VNS implants. We also examined how each algorithm improved source localization of somatosensory evoked fields in patients with VNS implants., Main Results: DSSP and tSSS algorithms have a similar ability to reject interference in both simulated and real MEG data if the origin location for tSSS is appropriately set. If the origin set for tSSS is inappropriate, the signal after tSSS can be distorted due to a mismatch between the internal region and the actual source space. Both DSSP and tSSS are able to remove large-amplitude artifacts from outside the brain. DSSP might be a better choice than tSSS when the choice of origin location for tSSS is difficult., Significance: Both DSSP and tSSS algorithms can recover distorted MEG recordings from people with intractable epilepsy and VNS implants, improving epileptic spike identification and source localization of both functional activity and epileptiform activity.

In this paper, we present a novel scanning algorithm, called Covariance Optimization Garnering Noise for Active Cancellation (COGNAC), for magnetoencephalography (MEG) and electroencephalography (EEG) source localization. COGNAC uses a probabilistic graphical generative model for describing sensor data. This novel generative model partitions contributions to sensor data from sources at a particular scan location and from sources outside the scan location, with corresponding multi-resolution variance parameters that are estimated from data. Maximizing a convex upper bound on the marginal likelihood of the data under this generative model results in a cost function that can be optimized efficiently. Importantly, this generative model enables learning of sensor noise without the need for additional baseline or pre-stimulus data. The resulting inference algorithm is quite robust to reconstruction of highly correlated sources and to the effect of high levels of interference and noise sources. Algorithm performance was compared to representative benchmark algorithms on both simulated and real brain activity. In simulations, performance of our novel algorithm is consistently superior to benchmarks. We also demonstrate that the new algorithm is robust to correlated brain activity present in real MEG/EEG data.

Magnetoencephalography (MEG) data is subject to many sources of environmental noise, and interference rejection is a necessary step in the processing of MEG data. Large amplitude interference caused by sources near the brain have been common in clinical settings and are difficult to reject. Artifact from vagal nerve stimulators (VNS) is a prototypical example. In this study, we describe a novel MEG interference rejection algorithm called dual signal subspace projection (DSSP), and evaluate its performance in clinical MEG data from people with epilepsy and implanted VNS. The performance of DSSP was evaluated in a retrospective cohort study of patients with epilepsy and VNS who had MEG scans for source localization of interictal epileptiform discharges. DSSP was applied to the MEG data and compared with benchmark for performance. We evaluated the clinical impact of interference rejection based on human expert detection and estimation of the location and time-course of interictal spikes, using an empirical Bayesian source reconstruction algorithm (Champagne). Clinical recordings, after DSSP processing, became more readable and a greater number of interictal epileptic spikes could be clearly identified. Source localization results of interictal spikes also significantly improved from those achieved before DSSP processing, including meaningful estimates of activity time courses. Therefore, DSSP is a valuable novel interference rejection algorithm that can be successfully deployed for the removal of strong artifacts and interferences in MEG., (Copyright © 2018. Published by Elsevier Inc.)

In this paper, we present a novel hierarchical multiscale Bayesian algorithm for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG). In particular, we present a solution to the source reconstruction problem for sources that vary in spatial extent. We define sensor data measurements using a generative probabilistic graphical model that is hierarchical across spatial scales of brain regions and voxels. We then derive a novel Bayesian algorithm for probabilistic inference with this graphical model. This algorithm enables robust reconstruction of sources that have different spatial extent, from spatially contiguous clusters of dipoles to isolated dipolar sources. We compare the new algorithm with several representative benchmarks on both simulated and real brain activities. The source locations and the correct estimation of source time courses used for the simulated data are chosen to test the performance on challenging source configurations. In simulations, performance of the novel algorithm shows superiority to several existing benchmark algorithms. We also demonstrate that the new algorithm is more robust to correlated brain activity present in real MEG and EEG data and is able to resolve distinct and functionally relevant brain areas with real MEG and EEG datasets., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Magnetoencephalography (MEG) has a well-recognized weakness at detecting deeper brain activities. This paper proposes a novel algorithm for selective detection of deep sources by suppressing interference signals from superficial sources in MEG measurements., Approach: The proposed algorithm combines the beamspace preprocessing method with the dual signal space projection (DSSP) interference suppression method. A prerequisite of the proposed algorithm is prior knowledge of the location of the deep sources. The proposed algorithm first derives the basis vectors that span a local region just covering the locations of the deep sources. It then estimates the time-domain signal subspace of the superficial sources by using the projector composed of these basis vectors. Signals from the deep sources are extracted by projecting the row space of the data matrix onto the direction orthogonal to the signal subspace of the superficial sources., Main Results: Compared with the previously proposed beamspace signal space separation (SSS) method, the proposed algorithm is capable of suppressing much stronger interference from superficial sources. This capability is demonstrated in our computer simulation as well as experiments using phantom data., Significance: The proposed bDSSP algorithm can be a powerful tool in studies of physiological functions of midbrain and deep brain structures.

Objective: In functional electrophysiological imaging, signals are often contaminated by interference that can be of considerable magnitude compared to the signals of interest. This paper proposes a novel algorithm for removing such interferences that does not require separate noise measurements., Approach: The algorithm is based on a dual definition of the signal subspace in the spatial- and time-domains. Since the algorithm makes use of this duality, it is named the dual signal subspace projection (DSSP). The DSSP algorithm first projects the columns of the measured data matrix onto the inside and outside of the spatial-domain signal subspace, creating a set of two preprocessed data matrices. The intersection of the row spans of these two matrices is estimated as the time-domain interference subspace. The original data matrix is projected onto the subspace that is orthogonal to this interference subspace., Main Results: The DSSP algorithm is validated by using the computer simulation, and using two sets of real biomagnetic data: spinal cord evoked field data measured from a healthy volunteer and magnetoencephalography data from a patient with a vagus nerve stimulator., Significance: The proposed DSSP algorithm is effective for removing overlapped interference in a wide variety of biomagnetic measurements.

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., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Source-space coherence analysis has become a popular method to estimate functional connectivity based on MEG/EEG. Source-space analysis involves solving the inverse problem, estimating the time courses of specific brain regions, and then examining the coherence between activities at different brain regions. However, source-space coherence analysis can be confounded by spurious coherence caused due to the leakage properties of the inverse algorithm employed. Such spurious coherence is typically manifested as an artifactual large peak around the seed voxel, called seed blur, in the resulting coherence images. This seed blur often obscures important details of brain interactions. This paper proposes the use of the imaginary part of the coherence to remove the spurious coherence caused by the leakage of an imaging algorithm. We present a theoretical analysis that explains how the use of imaginary part can remove this spurious coherence. We then present results from both computer simulations and experiments using resting-state MEG data which demonstrate the validity of our analysis.

The synchronous brain activity measured via magnetoencephalography (MEG) arises from current dipoles located throughout the cortex. Estimating the number, location, time-course, and orientation of these dipoles, called sources, remains a challenging task, one that is significantly compounded by the effects of source correlations and interference from spontaneous brain activity and sensor noise. Likewise, assessing the interactions between the individual sources, known as functional connectivity, is also confounded by noise and correlations in the sensor recordings. Computational complexity has been an obstacle to computing functional connectivity. This paper demonstrates the application of an empirical Bayesian method to perform source localization with MEG data in order to estimate measures of functional connectivity. We demonstrate that brain source activity inferred from this algorithm is better suited to uncover the interactions between brain areas as compared to other commonly used source localization algorithms.

In this paper, we develop a maximum-likelihood (ML) spatio-temporal blind source separation (BSS) algorithm, where the temporal dependencies are explained by assuming that each source is an autoregressive (AR) process and the distribution of the associated independent identically distributed (i.i.d.) innovations process is described using a mixture of Gaussians. Unlike most ML methods, the proposed algorithm takes into account both spatial and temporal information, optimization is performed using the expectation-maximization (EM) method, the source model is adapted to maximize the likelihood, and the update equations have a simple, analytical form. The proposed method, which we refer to as autoregressive mixture of Gaussians (AR-MOG), outperforms nine other methods for artificial mixtures of real audio. We also show results for using AR-MOG to extract the fetal cardiac signal from real magnetocardiographic (MCG) data.

This paper presents an analysis on the performance of the prewhitening beamformer when applied to magnetoencephalography (MEG) experiments involving dual (task and control) conditions. We first analyze the method's robustness to two types of violations of the prerequisites for the prewhitening method that may arise in real-life two-condition experiments. In one type of violation, some sources exist only in the control condition but not in the task condition. In the other type of violation, some signal sources exist both in the control and the task conditions, and that they change intensity between the two conditions. Our analysis shows that the prewhitening method is very robust to these nonideal conditions. In this paper, we also present a theoretical analysis showing that the prewhitening method is considerably insensitive to overestimation of the signal-subspace dimensionality. Therefore, the prewhitening beamformer does not require accurate estimation of the signal subspace dimension. Results of our theoretical analyses are validated in numerical experiments and in experiments using a real MEG data set obtained during self-paced hand movements.

Magnetoencephalography (MEG) and electroencephalography (EEG) sensor measurements are often contaminated by several interferences such as background activity from outside the regions of interest, by biological and non-biological artifacts, and by sensor noise. Here, we introduce a probabilistic graphical model and inference algorithm based on variational-Bayes expectation-maximization for estimation of activity of interest through interference suppression. The algorithm exploits the fact that electromagnetic recording data can often be partitioned into baseline periods, when only interferences are present, and active time periods, when activity of interest is present in addition to interferences. This algorithm is found to be robust and efficient and significantly superior to many other existing approaches on real and simulated data., (2007 John Wiley & Sons, Ltd)

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.

In this paper, we present a novel approach to imaging sparse and focal neural current sources from MEG (magnetoencephalography) data. Using the framework of Tikhonov regularization theory, we introduce a new stabilizer that uses the concept of controlled support to incorporate a priori assumptions about the area occupied by focal sources. The paper discusses the underlying Tikhonov theory and its relationship to a Bayesian formulation which in turn allows us to interpret and better understand other related algorithms.

Many tomographic source localization algorithms used in biomagnetic imaging assume, explicitly or sometimes implicitly, that the source activity at different brain locations are either independent or that the correlation structure between sources is known. Among these algorithms is a class of adaptive spatial filters known as beamformers, which have superior spatiotemporal resolution abilities. The performance of beamformers is robust to weakly coherent sources. However, these algorithms are extremely sensitive to the presence of strongly coherent sources. A frequent mode of failure in beamformers occurs with reconstruction of auditory evoked fields (AEFs), in which bilateral auditory cortices are highly coherent in their activation. Here, we present a novel beamformer that suppresses activation from regions with interfering coherent sources. First, a volume containing the interfering sources is defined. The lead field matrix for this volume is computed and reduced into a few significant columns using singular value decomposition (SVD). A vector beamformer is then constructed by rejecting the contribution of sources in the suppression region while allowing for source reconstruction at other specified regions. Performance of this algorithm was first validated with simulated data. Subsequent tests of this modified beamformer were performed on bilateral AEF data. An unmodified vector beamformer using whole head coverage misplaces the source medially. After defining a suppression region containing the temporal cortex on one side, the described method consistently results in clear focal activations at expected regions of the contralateral superior temporal plane.

To reconstruct neuromagnetic sources, the minimum-variance beamformer has been extended to incorporate the three-dimensional vector nature of the sources, and two types of extensions-the scalar- and vector-type extensions-have been proposed. This paper discusses the asymptotic signal-to-noise ratio (SNR) of the outputs of these two types of beamformers. We first show that these two types of beamformers give exactly the same output power and output SNR if the beamformer pointing direction is optimized. We then compare the output SNR of the beamformer with optimum direction to that of the conventional vector beamformer formulation where the beamformer pointing direction is not optimized. The comparison shows that the beamformer with optimum direction gives an output SNR superior to that of the conventional vector beamformer. Numerical examples validating the results of the analysis are presented.

The influence of external interference on neuromagnetic source reconstruction by adaptive beamformer techniques was investigated. In our analysis, we assume that the interference has the following two properties: First, it is additive and uncorrelated with brain activity. Second, its temporal behavior can be characterized by a few distinct activities, and as a result, the spatio-temporal matrix of the interference has a few distinctly large singular values. Namely, the interference can be modeled as a low-rank signal. Under these assumptions, our analysis shows that the adaptive beamformer techniques are insensitive to interference when its spatial singular vectors are so different from a lead field vector of a brain source that the generalized cosine between these two vectors is much smaller than unity. Four types of numerical examples verifying this conclusion are presented.

The influence of temporarily correlated source activities on neuromagnetic reconstruction by adaptive beamformer techniques was investigated. It is known that the spatial filter weight of an adaptive beamformer cannot perfectly block correlated signals. This causes two major influences on the reconstruction results: time course distortions and reductions in reconstructed signal intensities. Our theoretical analysis and numerical experiments both showed that the reduction in signal intensity for sources with a medium degree of correlation is small. The time-course distortion for such sources, however, may be discernible. Our analysis also showed that the magnitude correlation coefficient between two correlated sources can be accurately estimated by using the beamformer outputs. A method of retrieving the original time courses using estimated correlation coefficients was developed. Our numerical experiments demonstrated that reasonably accurate time courses can be retrieved from considerably distorted time courses even when the signal-to-noise ratio is low.

We have developed a method that incorporates the time-frequency characteristics of neural sources into magnetoencephalographic (MEG) source estimation. This method, referred to as the time-frequency multiple-signal-classification algorithm, allows the locations of neural sources to be estimated from any time-frequency region of interest. In this paper, we formulate the method based on the most general form of the quadratic time-frequency representations. We then apply it to two kinds of nonstationary MEG data: gamma-band (frequency range between 30-100 Hz) auditory activity data and spontaneous MEG data. Our method successfully detected the gamma-band source slightly medial to the N1m source location. The method was able to selectively localize sources for alpha-rhythm bursts at different locations. It also detected the mu-rhythm source from the alpha-rhythm-dominant MEG data that was measured with the subject's eyes closed. The results of these applications validate the effectiveness of the time-frequency MUSIC algorithm for selectively localizing sources having different time-frequency signatures.

In this paper, the details of processing algorithms used in a training program with language learning-impaired children (LLI's) are described. The training program utilized computer games, speech/language training exercises, books-on-tape and educational CD-ROM's. Speech tracks in these materials were processed using these algorithms. During a four week training period, recognition of both processed and normal speech in these children continually increased to near age-appropriate levels. We conclude that this form of processed speech is subject to profound perceptual learning effects and exhibits widespread generalization to normal speech. This form of learning and generalization contributes to the rehabilitation of temporal processing deficits and language comprehension in this subject population.