2012
DOI: 10.1016/j.neuroimage.2012.04.017
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Hierarchical Bayesian inference for the EEG inverse problem using realistic FE head models: Depth localization and source separation for focal primary currents

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Cited by 78 publications
(69 citation statements)
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References 82 publications
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“…However, the source space used in the current study includes subcortical grey matter and distributed source estimations calculate the current density at all solution points. Recent evidence demonstrate that deep sources can be reliably estimated from scalp-recorded electrophysiological data (Lucka et al 2012). Moreover, using the same inverse solution approach as in the current study, Michel et al (2004) demonstrated that deep interictal middle temporal lobe epileptic activity can be accurately localized with our methods.…”
Section: Discussionsupporting
confidence: 60%
“…However, the source space used in the current study includes subcortical grey matter and distributed source estimations calculate the current density at all solution points. Recent evidence demonstrate that deep sources can be reliably estimated from scalp-recorded electrophysiological data (Lucka et al 2012). Moreover, using the same inverse solution approach as in the current study, Michel et al (2004) demonstrated that deep interictal middle temporal lobe epileptic activity can be accurately localized with our methods.…”
Section: Discussionsupporting
confidence: 60%
“…However, the source space used in the current study includes subcortical gray matter, and distributed source estimations calculate the current density at all solution points. Recent evidence demonstrates that deep sources can be reliably estimated from scalp-recorded electrophysiological data Lucka et al, 2012).…”
Section: Electrical Source Estimationsmentioning
confidence: 99%
“…EMD is defined as the minimum cost that must be done to transform one normalized discrete signal into the other given the metric between the discrete points of the domain [40,37]. EMD has previously been used in the context of EEG imaging in [15,27] since it is suitable for comparing images with possibly non-overlapping support such as sparse vectors.…”
Section: Different Reconstructions and Comparison Metricsmentioning
confidence: 99%