A unified Bayesian framework for MEG/EEG source imaging

By David Paul Wipf, Srikantan S. Nagarajan. 2009

393 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications, 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. Martin Luessi
2. David M. Blei
3. W. D. Penny
4. Alp Kucukelbir
5. Jon D. McAuliffe
6. Francis R. Bach
7. Tong Zhang
8. Shivani Agarwal

In Computer Networks and Communications

1. Daniel S. Marcus
2. Mark Jenkinson
3. David M. Blei
4. W. D. Penny
5. Timothy R. Olsen
6. Jon D. McAuliffe

In Signal Processing

1. Alexandre Gramfort
2. Denis-Alexander Engemann
3. Mainak Jas
4. David M. Blei
5. Alp Kucukelbir
6. Jon D. McAuliffe
7. Christian Brodbeck
8. Daniel S. Marcus

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, background): “…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 interfaces (Sykacek, Roberts, and Stokes 2004), and factor models…”
• MNE software for processing MEG and EEG data (2013, methodology): “…either in the time domain, e.g., the minimum current estimate (MCE) (Matsuura and Okabe, 1995; Uutela et al., 1999), the γ -MAP estimate detailed in Wipf and Nagarajan (2009), and the mixed norm estimate (MxNE) (Gramfort et al., 2012; Ou et al., 2009), or in the time – frequency domain, e.g.,…”
• Informatics and Data Mining Tools and Strategies for the Human Connectome Project (2011, methodology): “This data will be analyzed in both sensor space and using state-of-the-art source localization methods (Wipf and Nagarajan, 2009; Ou et al., 2010) and using subject specific head models derived from anatomic MRI.”
• MEG and EEG data analysis with MNE-Python (2013, background): “…mixed-norm estimates (MxNE) (Gramfort et al., 2012), time– frequency mixed-norm estimates (TF-MxNE) (Gramfort et al., 2013b) that regularize the estimates in a time–frequency representation of the source signals, and a sparse Bayesian learning technique named γ -MAP (Wipf and Nagarajan, 2009).”
• Comparing Dynamic Causal Models using AIC, BIC and Free Energy (2012, background): “As noted in (Wipf and Nagarajan, 2009), because the Laplace approximation is not exactly equal to the Free Energy, the above lower bound property no longer holds.”