Reducing the effect of correlated brain sources in MEG using a linearly constrained spatial filter based on Minimum Norm

J. A. Sánchez, D. M. Halliday

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Magnetoencephalogram (MEG) studies rely on the use of spatial filters to find and extract the brain activity generated by neuronal currents. Two of the most used filters are the Linearly Constrained Minimum Variance beamformer (LCMV) and the Minimum Norm Estimates (MNE) non-adaptive spatial filter. These filters have different properties that can increase or decrease their performances, especially in the presence of correlated brain activity for the LCMV case, or in the presence of a poor signal to noise ratio (SNR) for the MNE case. This study introduces a filter based on the least-squares method to be used as a benchmark to decide when to use the MNE or the LCMV to increase the accuracy of the finding and estimation of the brain activity.

Original languageEnglish
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Number of pages5
ISBN (Print)9781479923908
Publication statusPublished - 1 Jan 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United Kingdom
Duration: 3 Nov 20136 Nov 2013


Conference2013 47th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited Kingdom
CityPacific Grove, CA

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