Signal processing in magnetoencephalography.

By J. Vrba, S. Robinson. Methods, 2001

751 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/94b11b4e0bec484376f810a557492fa776b40b28

How researchers see this paper

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

In Atomic and Molecular Physics, and Optics

1. Juha Simola
2. Samu Taulu
3. Matti J. Kajola
4. D. Bassett
5. E. Bullmore

In Cognitive Neuroscience

1. Micah M. Murray
2. C. Michel
3. D. Bassett
4. E. Bullmore
5. Olaf Sporns
6. William C. Gaetz
7. Yong He
8. Douglas O. Cheyne

In Signal Processing

1. Juha Simola
2. Samu Taulu
3. E. Bullmore
4. Matti J. Kajola
5. Micah M. Murray
6. D. Bassett
7. C. Michel
8. Riitta Hari

Citations in the corpus

• Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements (2006, methodology): “These methods include e.g. gradiometric coil configurations (Zimmerman 1977), reference sensors (Vrba and Robinson 2001), signal space projection (SSP) (Uusitalo and Ilmoniemi 1997), and most recently the signal space separation (SSS) method (Taulu and Kajola 2005, Taulu et al 2005).”
• Removal of magnetoencephalographic artifacts with temporal signal‐space separation: Demonstration with single‐trial auditory‐evoked responses (2009, neutral): “Thus, it is possible to obtain more accurate estimates of the artifacts in MEG signals than, e.g., by artifact-suppression methods based on reference channels located typically more than 10 cm from the MEG sensors [Vrba and Robinson, 2001].”
• Applications of the signal space separation method (2005, background): “The potential with is associated with the field of a magnetic monopole and is therefore excluded from the expansion.”
• Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event‐related beamforming approach (2006, background): “…and topographic maps were clearly distorted by low-frequency contamination due to eye movements or environmental noise (e.g., automobile traffic from a nearby road) even when utilizing combined adaptive balancing and third-order synthetic gradient noise reduction [Vrba and Robinson, 2001].”
• EEG source imaging. (2004, background): “It has been postulated that SAM does not suffer from the same limitations as the linear inverse solutions (Vrba and Robinson, 2001).”
• Brain graphs: graphical models of the human brain connectome. (2011, methodology): “Thus we can see that a preprocessing step (source reconstruction) that might seem attractive for defining the nodes of an MEG network could have severe effects on the covariance between nodes that will later be used to define the edges of the network (Vrba & Robinson 2001).”
• Cortical Oscillatory Activity and Motor Control in Pediatric Stroke Patients With Hemidystonia (2025, unknown): “The Synthetic Aperture Magnetometry (SAM) beamformer algo-rithm (Vrba and Robinson 2001), implemented in the BrainWave Matlab toolbox (available at https:// github. com/ cheyn elab) xxx (Jobst et al. 2018), was used to localize and quantify frequency-specific changes across the entire brain with 4…”