Abstract
We present an approach to handle forward
model uncertainty for EEG source reconstruction. A
stochastic forward model representation is motivated
by the many random contributions to the path from
sources to measurements including the tissue conductivity
distribution, the geometry of the cortical surface,
and electrode positions. We first present a hierarchical
Bayesian framework for EEG source localization
that jointly performs source and forward model reconstruction
(SOFOMORE). Secondly, we evaluate the
SOFOMORE approach by comparison with source reconstruction
methods that use fixed forward models.
Analysis of simulated and real EEG data provide evidence
that reconstruction of the forward model leads
to improved source estimates.
Original language | English |
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Journal | Journal of Signal Processing Systems |
Volume | 65 |
Issue number | 3 |
Pages (from-to) | 431-444 |
ISSN | 1939-8018 |
DOIs | |
Publication status | Published - 2011 |
Keywords
- Inverse problem
- Distributed models
- Variational Bayes
- Forward model reconstruction
- EEG
- Source localization