Comparison and improvements of LCMV and MUSIC source localization techniques for use in real clinical environments

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• 作者：A. de Hoyos^a ; ^{adehoyos@externos.adif.es} ; J. Portillo^b ; ¹ ; ^{javierp@grpss.ssr.upm.es} ; I. Portillo^c ; ² ; ^{iportillo@cee.upcomillas.es} ; P. Marí ; n^g ; ³ ; ^{pmarin@externos.adif.es} ; F. Maestú ; ^d ; ⁴ ; ^{fmaestuu@psi.ucm.es} ; J. Poch-Broto^e ; ⁵ ; ^{jpoch.hcsc@salud.madrid.org} ; T. Ortiz^f ; ⁶ ; ^{tortiz@med.ucm.es} ; Antonio Hernando^g ; ³ ; ^{antonio.hernando@externos.adif.es}
• 关键词：Magnetoencephalography ; Spatial beamforming ; High-resolution spatial filtering ; Multiple Signal Classification (MUSIC) ; Linearly Constrained Minimum Variance (LCMV)
• 刊名：Journal of Neuroscience Methods
• 出版年：2012
• 期刊代码：33_01650270
• 类别：neu
• 出版时间：15 April, 2012
• 卷：205
• 期：2
• 页码：312-323
• 文件大小：1998 K

摘要

The present work shows some improvements realized on practical aspects of the implementation of Singular Value Decomposition (SVD) methods to localize the sources of neural activity by means of magnetoencephalograph (MEG). Two methods have been improved and compared i.e. a spatial filter, the Linearly Constrained Minimum Variance Beamformer (LCMV) method, and a signal subspace method that is an implementation of the MUSIC (Multiple Signal Classification) method due to . It also shows the performance of both methods comparing three different averaging procedures. The influence of the correct selection of the noise subspace dimension has been analyzed. Using acoustic stimulus for real patient measurements, we discuss the relevant differences of both methods and propose an adequate strategy for future diagnosis based on correct source localization.