Hierarchical Bayesian estimation for MEG inverse problem

By Masa-aki Sato, Taku Yoshioka, Shigeki Kajihara, Keisuke Toyama, Naokazu Goda, Kenji Doya, Mitsuo Kawato. 2004

292 citations recorded by the source corpus.

Subfields: Computational Mechanics, Radiology, Nuclear Medicine and Imaging, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. David M. Blei
2. W. D. Penny
3. Alp Kucukelbir
4. Jon D. McAuliffe
5. Francis R. Bach
6. Tong Zhang
7. Shivani Agarwal
8. Vladimir Koltchinskii

In Radiology, Nuclear Medicine and Imaging

1. Karl J. Friston
2. W. D. Penny
3. Jean Daunizeau
4. Klaas E. Stephan
5. Stefan J. Kiebel
6. Maria João Duarte Rosa
7. Alexander P. Leff
8. T. Schofield

In Signal Processing

1. Karl J. Friston
2. Jean Daunizeau
3. Stefan J. Kiebel
4. Klaas E. Stephan
5. W. D. Penny
6. Christophe L. M. Phillips
7. David M. Blei
8. Alp Kucukelbir

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, background): “…applications of variational inference to neuro-science include hierarchical models of multiple subjects (Wool-rich et al. 2004), spatial models (Sato et al. 2004; Zumer et al. 2007; Kiebel et al. 2008; Wipf and Nagarajan 2009; Lashkari et al. 2012; Nathoo et al. 2014), brain-computer…”
• Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control. (2011, methodology): “These techniques are widely utilized in estimating brain activities, brain decoding, and brain machine interfaces [74–76].”
• Multiple sparse priors for the M/EEG inverse problem (2008, unknown): “However, these models can also be extended hierarchically by inducing hyper-priors on the hyperparameters themselves (see Trujillo-Barreto et al., 2004; Sato et al., 2004; Daunizeau and Friston, 2007).”
• Comparing Families of Dynamic Causal Models (2010, methodology): “The Free Energy is but one approximation to the model evidence, albeit one that is widely used in neuroimaging [29,30].”
• Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG (2008, methodology): “It was also used by Sato et al. (2004) to implement automatic relevance determination (ARD) to 'switch off' redundant sources in an imaging context.”
• A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data (2006, unknown): “Several tomographic algorithms use a Bayesian framework to explicitly incorporate prior assumptions about source configuration in the model (Sato et al., 2004; Phillips et al., 2005; Mattout et al., 2006).”
• A unified Bayesian framework for MEG/EEG source imaging (2009, unknown): “Unfortunately, because these EM updates are prohibitively slow in practice, some heuristics are proposed to facilitate the estimation process (Sato et al., 2004).”