Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique

By K. Sekihara, Srikantan S. Nagarajan, David Poeppel, Alec Marantz, Yasushi Miyashita. IEEE Transactions on Biomedical Engineering, 2001

395 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/426b119bdb077ba2005eea33eb236b08142e0270

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Citations in the corpus

Electrophysiological Imaging of Brain Activity and Connectivity – Challenges and Opportunities (2011, methodology): “A beamformer [48], [49] uses spatial filtering to selectively pass brain activity from a certain region while attenuates the interference originated”
Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution (2012, positive): “…2004) can be used to determine the optimum source orientation, or one can simply compute the beamformer weights for the two tangential orientation components for each source (or all 3 orthogonal components in the case of electroencephalography (EEG); Sekihara et al., 2001; van Veen et al., 1997).”
Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction (2005, methodology): “The weight-normalized version of the minimum-variance filter has been used for MEG source reconstruction (Sekihara et al., 2001).”
The co-occurrence of multisensory facilitation and cross-modal conflict in the human brain. (2011, positive): “The “nonlinear beamformer” or synthetic aperture magnetometry (SAM) technique was used to model the source of the measured magnetic field (Robinson and Vrba 1998; Sekihara et al. 2001).”
A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data (2006, neutral): “We finish the Methods by showing that an established source localization method, the minimum variance adaptive beamformer (MVAB) (Sekihara et al., 2001), is an approximation of our framework.”
Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event‐related beamforming approach (2006, methodology): “differed in that they applied the resulting beamformer weights to only the estimated signal subspace of the measurement covariance matrix [Sekihara et al., 2001], possibly resulting in higher signal to noise of the resulting images, although this approach requires a priori specification of the dimen”
Fast transient networks in spontaneous human brain activity (2014, methodology): “…at each MEG sensor (Sarvas, 1987; Huang et al., 1999) ϕ ϕ ϕ ϕ 1T T 1 T 1 , , , ,=r r r rw h C h h C −− − where, hr,φ is the (N × 1) projection of the (N × 3) lead fields Hr,Φ to the dipole orientation φ that maximizes the projected signal-to-noise ratio, computed as in Sekihara et al. (2001).”