Paper Info
Title
Brain source localization based on fast fully adaptive approach.
Abstract
In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization (beamforming) methods often fail when the number of observations is small. This is particularly true when measuring evoked potentials, especially when the number of electrodes is large. Due to the nonstationarity of the EEG/MEG, an adaptive capability is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This paper develops and tests a new multistage adaptive processing for brain source localization that has been previously used for radar statistical signal processing application with uniform linear antenna array. This processing, referred to as the fast fully adaptive (FFA) approach, could significantly reduce the required sample support and computational complexity, while still processing all available DoFs. The performance improvement offered by the FFA approach in comparison to the fully adaptive minimum variance beamforming (MVB) with limited data is demonstrated by bootstrapping simulated data to evaluate the variability of the source location.
Year
DOI
Venue
2012
10.1109/EMBC.2012.6347171
EMBC
Keywords
Field
DocType
fast fully adaptive approach,magnetoencephalogram,minimum variance beamforming,bioelectric potentials,eeg signal,electroencephalogram,electroencephalography,medical signal processing,evoked potential,array signal processing,fast fully adaptive processing,computational complexity,magnetoencephalography,adaptive degrees of freedom,brain source localization
Radar,Beamforming,Computer science,Bootstrapping,Antenna array,Electronic engineering,Statistical signal processing,Magnetoencephalography,Computational complexity theory,Performance improvement
Conference
Volume
ISSN
ISBN
2012
1557-170X
978-1-4577-1787-1
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Maryam Ravan102.37
James Reilly245743.42