A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data

By Johanna M. Zumer, Hagai Thomas Attias, K. Sekihara, Srikantan S. Nagarajan. 2006

70 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, 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 Artificial Intelligence

1. Karl J. Friston
2. Stefan J. Kiebel
3. Mark William Woolrich
4. Marta Isabel Garrido
5. Rosalyn J. Moran
6. Matthew J. Brookes
7. Jean Daunizeau
8. Klaas E. Stephan

In Cognitive Neuroscience

1. Karl J. Friston
2. Mark William Woolrich
3. Stefan J. Kiebel
4. Marta Isabel Garrido
5. Rosalyn J. Moran
6. Stephen M. Smith
7. Jean Daunizeau
8. W. D. Penny

In Signal Processing

1. Karl J. Friston
2. Stefan J. Kiebel
3. Marta Isabel Garrido
4. Rosalyn J. Moran
5. Matthew J. Brookes
6. Mark William Woolrich
7. Jean Daunizeau
8. W. D. Penny

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, neutral): “…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 interfaces (Sykacek,…”
• Dynamic causal modeling for EEG and MEG (2009, background): “These allow for the formal introduction of constraints or priors that ensure robust parameter estimation [Auranen et al., 2007; Nummenmaa et al., 2007a; Penny et al., 2007; Zumer et al., 2007].”
• Investigating the electrophysiological basis of resting state networks using magnetoencephalography (2011, unknown): “(A, ii) Right lateral parietal [MNI (42, −70, 24) mm] and prefrontal [MNI (18, 20, 40) mm] cortices.”
• Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage (2012, background): “4A. Future work in this area could make use of the dual-core beamformer (Diwakar et al., 2011) or other inverse problem solutions not affected by correlated sources (Wipf et al., 2010; Zumer et al., 2007).”
• Measuring functional connectivity using MEG: Methodology and comparison with fcMRI (2011, unknown): “This, coupled with the high number of sensors (~300 in modern systems) and advanced source reconstruction algorithms (Robinson and Vrba, 1998; Zumer et al., 2007; Wipf et al., 2010), makes MEG data more appropriate for projection into source space.”