Reduced spatially correlated noise influence using subspace source localization method FINES.
We have developed a high resolution subspace approach for EEG source localization within a realistic geometry inhomogeneous head model. The present study aims to reduce the influence caused by spatially correlated noise from background activities using FINES. Computer simulations were conducted on the realistic geometry head volume conductor model and compared with the classic MUSIC algorithm. The FINES approach was also applied to source localization of motor potentials induced by the execution of finger movement in a human subject. The present results suggest that FINES is insensitive to spatially correlated noise, and has enhanced performance as compared with MUSIC.
Ding
Lei
L
University of Minnesota, MN, USA.
Xu
Xiaoliang
X
Xu
Bobby
B
Ni
Ying
Y
He
Bin
B