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Bayesian inference for neural electromagnetic source localization: analysis of MEG visual evoked activity
1999
Medical Imaging 1999: Image Processing
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BAYESIAN INFERENCE APPLIED T O THE EEG/MEG INVERSE PROBLEM
Activity Model In applying the methods of Bayesian inference to the EEG/MEG inverse problem we constructed a model for regions of activation ...
Our Bayesian inference analysis was applied separately to the data for each visual field stimulus at 110 ms poststimulus latency; a latency that should include robust activation of the calcarine region ...
doi:10.1117/12.348596
dblp:conf/miip/SchmidtGW99
fatcat:a5wlfubpsbcntjrdwokrkgve2i
Population-level inferences for distributed MEG source localization under multiple constraints: Application to face-evoked fields
2007
NeuroImage
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We address some key issues entailed by population inference about responses evoked in distributed brain systems using magnetoencephalography (MEG). ...
conditions, and (ii) whether to accommodate differences in source orientation by using signed or unsigned (absolute) estimates of source activity. ...
Introduction This paper is about the analysis of multi-subject MEG data using distributed source estimates. ...
doi:10.1016/j.neuroimage.2007.07.026
pmid:17888687
fatcat:zl4nioeoxrey7comjgx7qgbyd4
A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities
[article]
2019
arXiv
pre-print
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In this review we provide enough evidence that the effects of psychiatric drugs on the activity of brain sources have not been enough investigated, which provides motivation for consideration in the future ...
Electroencephalography (EEG) is a popular non-invasive electrophysiological technique of relatively very high time resolution which is used to measure electric potential of brain neural activity. ...
Timsari, "Imaging variation-based methods for the analysis of extended brain sources,"
neural activity using MEG and EEG," IEEE Engineering in in 2014 22nd ...
arXiv:1910.07980v1
fatcat:rxc6u3d3tvhqpnw6lkt22o44tu
Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics
2018
Annual Review of Biomedical Engineering
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Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. ...
It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. ...
SOURCE IMAGING APPROACHES AND APPLICATIONS Neural activity of interest to EEG and MEG includes ongoing activity in the absence of any task, or the responses evoked or induced by various events. ...
doi:10.1146/annurev-bioeng-062117-120853
pmid:29494213
pmcid:PMC7941524
fatcat:xypqgl7snbbnnidrn5ddepj6tu
Hierarchical multiscale Bayesian algorithm for robust MEG/EEG source reconstruction
2018
NeuroImage
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We then derive a novel Bayesian algorithm for probabilistic inference with this graphical model. ...
In this paper, we present a novel hierarchical multiscale Bayesian algorithm for electromagnetic brain imaging using magnetoencephalography (MEG) and electroencephalography (EEG). ...
We would also like to thank Julia Owen for the support of MEG data simulation and evaluation, Hagai Attias for inspiration and early discussions, and Inez Raharjo for editing. ...
doi:10.1016/j.neuroimage.2018.07.056
pmid:30059734
pmcid:PMC6214686
fatcat:sm4x7k7ri5fzrp3yiirjzsbeea
Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents
2012
NeuroImage
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Our work examines the performance of fully-Bayesian inference methods for HBM for source configurations consisting of few, focal sources when used with realistic, high resolution Finite Element (FE) head ...
Especially the recovery of brain networks involving deep-lying sources by means of EEG/MEG recordings is still a challenging task for any inverse method. ...
Kugel (Department of Clinical Radiology, University of Münster, Germany) for the measurement of the MRI and A. Janssen, S. ...
doi:10.1016/j.neuroimage.2012.04.017
pmid:22537599
fatcat:vs33kxcpsrgpni77pictrdciei
Proceedings: ISBET 200 – 14th World Congress of the International Society for Brain Electromagnetic Topography, November 19-23, 2003
2003
Brain Topography
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A high-performance, robust tool for electromagnetic source localization and visualization, Curry integrates multiple, complementary modalities (EEG and MEG; MRI, fMRI, CT, PET or SPECT) in a single software ...
The BESA (Brain Electrical Source Analysis) program provides a large variety of tools for the complete analysis of EEG and MEG recordings. ...
Reference [2] extended their work to a spatiotemporal Bayesian inference analysis of the full spatial-temporal MEG/EEG data set, using their extended region model for neural activity. ...
doi:10.1023/b:brat.0000019284.29068.8d
fatcat:tpvp3dcojrczjkuzcu3xefyizy
Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC
2008
NeuroImage
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In this paper we introduce methods of Bayesian inference as a way to integrate different forms of brain imaging data in a probabilistic framework. ...
We formulate Bayesian integration of magnetoencephalography (MEG) data and functional magnetic resonance imaging (fMRI) data by incorporating fMRI data into a spatial prior. ...
This led us to develop a combined MEG/EEG/fMRI Bayesian inference source analysis. ...
doi:10.1016/j.neuroimage.2007.12.029
pmid:18314351
pmcid:PMC2929566
fatcat:pusjqwqdhbfe5edcoogrziod3e
Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG
2008
IEEE Reviews in Biomedical Engineering
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Electrophysiological and hemodynamic/metabolic signals reflect distinct but closely coupled aspects of the underlying neural activity. ...
in the understanding and modeling of neurovascular coupling and in the methodologies for the fMRI-EEG/MEG simultaneous recording. ...
Such inference is classic in terms of statistics, as opposed to more recent methods based on the Bayesian inference which provides the posterior probability that the voxel is activated given the data ...
doi:10.1109/rbme.2008.2008233
pmid:20634915
pmcid:PMC2903760
fatcat:6hogffiwvrbonmxn2swkyqstde
Evoked brain responses are generated by feedback loops
2007
Proceedings of the National Academy of Sciences of the United States of America
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Furthermore, we were able to quantify the contribution of backward connections to evoked responses and to source activity, again as a function of peristimulus time. ...
This is the theoretical cornerstone of most modern theories of perceptual inference and learning. ...
We thank David Bradbury for technical support and the volunteers for participating in this study, Oliver Hulme for comments on the manuscript, and Marcia Bennett for preparing the manuscript. ...
doi:10.1073/pnas.0706274105
pmid:18087046
pmcid:PMC2409249
fatcat:nzacb3ooyvenhgoqhd2nlnx6hm
Dynamic causal modeling for EEG and MEG
2009
Human Brain Mapping
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V C 2009 Wiley-Liss, Inc. r Dynamic Causal Modeling for EEG and MEG r r 1867 r ...
We present a review of dynamic causal modeling (DCM) for magneto-and electroencephalography (M/EEG) data. ...
ACKNOWLEDGMENTS We thank Katharina von Kriegstein for helpful comments. ...
doi:10.1002/hbm.20775
pmid:19360734
fatcat:tnrjkb42nbernor22ujooszzo4
Rapid Interactions between the Ventral Visual Stream and Emotion-Related Structures Rely on a Two-Pathway Architecture
2008
Journal of Neuroscience
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neural biophysics. ...
Visual attention can be driven by the affective significance of visual stimuli before full-fledged processing of the stimuli. ...
Bayesian inversion scheme to estimate the neural parameters of the neural-mass models based on the measured evoked responses. ...
doi:10.1523/jneurosci.3476-07.2008
pmid:18337409
pmcid:PMC6670659
fatcat:sw2vpctw3ferzgk322qej77cka
Adaptive neural network classifier for decoding MEG signals
2019
NeuroImage
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Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. ...
We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. ...
of an unknown number of simultaneously active neural sources. ...
doi:10.1016/j.neuroimage.2019.04.068
pmid:31059799
pmcid:PMC6609925
fatcat:orsoefql25hm7fexgqtakpki6y
Bayesian Modelling of Induced Responses and Neuronal Rhythms
2016
Brain Topography
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We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses-and the identification of important sources ...
of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. ...
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s10548-016-0526-y
pmid:27718099
fatcat:pswqqxexfjbw7k46qbjxauz2ry
Signal Propagation in the Human Visual Pathways: An Effective Connectivity Analysis
2015
Journal of Neuroscience
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Consistent with hierarchical early visual processing, the model disclosed and quantified the neural temporal dynamics across the identified activity sources. ...
A recurrent forward-backward connectivity model, consisting of multiple interacting brain regions identified by EEG source localization aided by fMRI spatial priors, best accounted for the data dynamics ...
A, B, Time course source activities obtained from DCM analysis of grand mean VEPs. Results representing neural source activities with one zoomed area. ...
doi:10.1523/jneurosci.2269-15.2015
pmid:26424894
fatcat:axwpsfz3vzb5fn3skca36japnm
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