scholarly journals A Unified Bayesian Framework for MEG/EEG Source Imaging

2015 ◽  
pp. 119-137 ◽  
Author(s):  
Kensuke Sekihara ◽  
Srikantan S. Nagarajan
Keyword(s):  
Bayesian Framework ◽  
Source Imaging ◽  
Eeg Source Imaging

scholarly journals A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals

2019 ◽  
Author(s):  
Alejandro Ojeda ◽  
Marius Klug ◽  
Kenneth Kreutz-Delgado ◽  
Klaus Gramann ◽  
Jyoti Mishra
Keyword(s):  
Empirical Bayes ◽  
Data Cleaning ◽  
Source Separation ◽  
Bayesian Framework ◽  
Retrosplenial Cortex ◽  
Brain Atlas ◽  
Electromyographic Activity ◽  
Source Imaging ◽  
Eeg Source Imaging ◽  
Wide Range

AbstractElectroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, source separation, and localization. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust EEG source estimates on a wide range of experimental paradigms. In this paper, we propose a unifying Bayesian framework in which these apparently dissimilar problems can be understood and solved in a principled manner using a single algorithm. We explicitly model the effect of non-brain sources by augmenting the lead field matrix with a dictionary of stereotypical artifact scalp projections. We propose to populate the artifact dictionary with non-brain scalp projections obtained by running Independent Component Analysis (ICA) on an EEG database. Within a parametric empirical Bayes (PEB) framework, we use an anatomical brain atlas to parameterize a source prior distribution that encourages sparsity in the number of cortical regions. We show that, in our inversion algorithm, PEB+ (PEB with the addition of artifact modeling), the sparsity prior has the property of inducing the segregation of the cortical activity into a few maximally independent components with known anatomical support. Artifacts produced by electrooculographic and electromyographic activity as well as single-channel spikes are also segregated into their respective components. Of theoretical relevance, we use our framework to point out the connections between Infomax ICA and distributed source imaging. We use real data to demonstrate that PEB+ outperforms Infomax for source separation on short segments of data and, unlike the popular Artifact Subspace Removal algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations. In this example, we run PEB+ followed by the spectral analysis of the activity in the retrosplenial cortex, largely replicating the findings of previous experimental literature.

scholarly journals A unified Bayesian framework for MEG/EEG source imaging

NeuroImage ◽  
2009 ◽  
Vol 44 (3) ◽  
pp. 947-966 ◽  
Author(s):  
David Wipf ◽  
Srikantan Nagarajan
Keyword(s):  
Bayesian Framework ◽  
Source Imaging ◽  
Eeg Source Imaging

EEG Source Imaging (ESI) utility in clinical practice

2020 ◽  
Vol 65 (6) ◽  
pp. 673-682
Author(s):  
Pegah Khosropanah ◽  
Eric Tatt-Wei Ho ◽  
Kheng-Seang Lim ◽  
Si-Lei Fong ◽  
Minh-An Thuy Le ◽  
...  
Keyword(s):  
Focal Epilepsy ◽  
Cost Effective ◽  
Head Model ◽  
Computational Technique ◽  
Epileptogenic Zone ◽  
Clinical Tool ◽  
Source Imaging ◽  
Localization Accuracy ◽  
Eeg Source Imaging ◽  
Surgical Evaluation

AbstractEpilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73–91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.

scholarly journals ConvDip: A convolutional neural network for better M/EEG Source Imaging

2020 ◽  
Author(s):  
Lukas Hecker ◽  
Rebekka Rupprecht ◽  
Ludger Tebartz van Elst ◽  
Juergen Kornmeier
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Convolutional Neural Network ◽  
Brain Activity ◽  
Clinical Diagnostics ◽  
Dipole Model ◽  
Source Imaging ◽  
Eeg Source Imaging ◽  
Eeg Data ◽  
Inverse Solutions

AbstractEEG and MEG are well-established non-invasive methods in neuroscientific research and clinical diagnostics. Both methods provide a high temporal but low spatial resolution of brain activity. In order to gain insight about the spatial dynamics of the M/EEG one has to solve the inverse problem, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipoles sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods (eLORETA and LCMV beamforming) on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. (4) It produces plausible inverse solutions for real-world EEG recordings and needs less than 40 ms for a single forward pass. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG and MEG data, with high relevance for clinical applications, e.g. in epileptology and real time applications.

scholarly journals Influence of Patient-Specific Head Modeling on EEG Source Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yohan Céspedes-Villar ◽  
Juan David Martinez-Vargas ◽  
G. Castellanos-Dominguez
Keyword(s):  
Neurological Diseases ◽  
Structural Data ◽  
Problem Formulation ◽  
Patient Specific ◽  
Source Imaging ◽  
Eeg Source Imaging ◽  
Deep Sources ◽  
The Individual ◽  
Tissue Compartments ◽  
The Brain

Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject’s head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources.

scholarly journals EEG Source Imaging Indices of Cognitive Control Show Associations with Dopamine System Genes

2017 ◽  
Vol 31 (3) ◽  
pp. 392-406 ◽  
Author(s):  
G. McLoughlin ◽  
J. Palmer ◽  
S. Makeig ◽  
N. Bigdely-Shamlo ◽  
T. Banaschewski ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Dopamine System ◽  
Source Imaging ◽  
Eeg Source Imaging

scholarly journals Multi-subject MEG/EEG source imaging with sparse multi-task regression

NeuroImage ◽  
2020 ◽  
Vol 220 ◽  
pp. 116847
Author(s):  
Hicham Janati ◽  
Thomas Bazeille ◽  
Bertrand Thirion ◽  
Marco Cuturi ◽  
Alexandre Gramfort
Keyword(s):  
Source Imaging ◽  
Eeg Source Imaging

EEG source imaging in epilepsy—practicalities and pitfalls

2012 ◽  
Vol 8 (9) ◽  
pp. 498-507 ◽  
Author(s):  
Kitti Kaiboriboon ◽  
Hans O. Lüders ◽  
Mehdi Hamaneh ◽  
John Turnbull ◽  
Samden D. Lhatoo
Keyword(s):  
Source Imaging ◽  
Eeg Source Imaging
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