Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization
Two techniques are proposed to alleviate the computational burden of MUltiple SIgnal Classification (MUSIC) algorithm applied to Electroencephalogram (EEG) source localization. A significant reduction was achieved by parsing the cortex surface into smaller regions and nominating only a few regions f...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
12.07.2017
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Subjects | |
Online Access | Get full text |
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Summary: | Two techniques are proposed to alleviate the computational burden of MUltiple
SIgnal Classification (MUSIC) algorithm applied to Electroencephalogram (EEG)
source localization. A significant reduction was achieved by parsing the cortex
surface into smaller regions and nominating only a few regions for the
exhaustive search inherent in the MUSIC algorithm. The nomination procedure
involves a dictionary learning phase in which each region is assigned an atom
matrix. Moreover, a dimensionality reduction step provided by excluding some of
the electrodes is designed such that the Cramer-Rao bound of localization is
maintained. It is shown by simulation that computational complexity of the
MUSIC-based localization can be reduced by up to $80\%$. |
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DOI: | 10.48550/arxiv.1707.03536 |