Application of an MEG eigenspace beamformer to reconstructing spatio-temporal activities of neural sources.
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.
Sekihara
Kensuke
K
Department of Electronic Systems and Engineering, Tokyo Metropolitan Institute of Technology, Asahigaoka 6-6, Hino, Tokyo 191-0065, Japan. ksekiha@cc.tmit.ac.jp
Nagarajan
Srikantan S
SS
Poeppel
David
D
Marantz
Alec
A
Miyashita
Yasushi
Y
D005072:Evoked Potentials, Auditory / Q000502:physiology; D006801:Humans; D015225:Magnetoencephalography / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data*; D008297:Male; D008959:Models, Neurological; D013702:Temporal Lobe / Q000502:physiology