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The subspace source localization approach, i.e., first principle vectors (FINE), is able to enhance the spatial resolvability and localization accuracy for closely-spaced neural sources from EEG and MEG measurements. Computer simulations were conducted to evaluate the performance of the FINE algorithm in an inhomogeneous realistic geometry head model under a variety of conditions. The source localization...
We investigated the source localization performance of the Laplacian weighted minimum norm (LWMN) estimate technique in a realistic geometry (RG) head model in the present study. We simulated current sources at different brain regions with various noise levels. The present results show there is no obvious depth dependency on the three-dimensional (3D) source estimation. The average source localization...
We have developed a new algorithm, FINE, to enhance the spatial resolution and localization accuracy for closely-spaced sources, in the framework of the subspace source localization. Computer simulations were conducted in the present study to evaluate the performance of FINE, as compared with classic subspace source localization algorithms, i.e. MUSIC and RAP-MUSIC, in a realistic geometry head model...
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...
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