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arXiv · 1707.03536

Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization

Seyede Mahya Safavi·Beth Lopour·Pai H. Chou
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Abstract

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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Seyede Mahya Safavi, Beth Lopour, Pai H. Chou. 2017-07-12. Application of Dictionary Learning in Alleviating Computational Burden of EEG Source Localization. https://arxiv.org/abs/1707.03536

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