Open AccessApplication of an MEG eigenspace beamformer to reconstructing spatio‐temporal activities of neural sources
Author(s) -
Sekihara Kensuke,
Nagarajan Srikantan S.,
Poeppel David,
Marantz Alec,
Miyashita Yasushi
Publication year - 2002
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.10019
Subject(s) - covariance matrix , eigenvalues and eigenvectors , adaptive beamformer , noise (video) , subspace topology , covariance , projection (relational algebra) , signal subspace , algorithm , computer science , signal to noise ratio (imaging) , signal (programming language) , pattern recognition (psychology) , mathematics , beamforming , artificial intelligence , statistics , physics , quantum mechanics , image (mathematics) , programming language
We have applied the eigenspace‐based beamformer to reconstruct spatio‐temporal activities of neural sources from MEG data. The weight vector of the eigenspace‐based beamformer is obtained by projecting the weight vector of the minimum‐variance beamformer onto the signal subspace of a measurement covariance matrix. This projection removes the residual noise‐subspace component that considerably degrades the signal‐to‐noise ratio (SNR) of the beamformer output when errors in estimating the sensor lead field exist. Therefore, the eigenspace‐based beamformer produces a SNR considerably higher than that of the minimum‐variance beamformer in practical situations. The effectiveness of the eigenspace‐based beamformer was validated in our numerical experiments and experiments using auditory responses. We further extended the eigenspace‐based beamformer so that it incorporates the information regarding the noise covariance matrix. Such a prewhitened eigenspace beamformer was experimentally demonstrated to be useful when large background activity exists. Hum. Brain Mapping 15:199–215, 2002. © 2002 Wiley‐Liss, Inc.