AI Article Synopsis
- This work focuses on enhancing Singular Value Decomposition (SVD) methods for localizing neural activity using magnetoencephalography (MEG).
- The study compares two improved methods: the Linearly Constrained Minimum Variance Beamformer (LCMV) and the MUSIC (Multiple Signal Classification) technique.
- It evaluates the methods' performances with three averaging procedures and analyzes how selecting the right noise subspace dimension can impact results, ultimately suggesting strategies for better future diagnoses.
Article Abstract
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 Mosher et al. (1992). 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.
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| http://dx.doi.org/10.1016/j.jneumeth.2012.01.012 | DOI Listing |
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