A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration

Richard N Henson, Daniel G Wakeman, Vladimir eLitvak, Karl J Friston

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DOI: 10.3389/fnhum.2011.00076
Published: 8
Container: Frontiers in Human Neuroscience
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fnhum.2011.00076,
  title = {A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration},
  author = {Richard N Henson and Daniel G Wakeman and Vladimir eLitvak and Karl J Friston},
  year = {2011},
  journal = {Frontiers in Human Neuroscience},
  doi = {10.3389/fnhum.2011.00076},
  url = {https://doi.org/10.3389/fnhum.2011.00076}
}

RIS

TY  - JOUR
TI  - A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration
AU  - Richard N Henson
AU  - Daniel G Wakeman
AU  - Vladimir eLitvak
AU  - Karl J Friston
PY  - 2011
JO  - Frontiers in Human Neuroscience
DO  - 10.3389/fnhum.2011.00076
UR  - https://doi.org/10.3389/fnhum.2011.00076
ER  -

APA

Henson, R. N., Wakeman, D. G., eLitvak, V., & Friston, K. J. (2011). A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2011.00076

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doaj · retrieved 2026-09-25T06:34:45.804Z