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

Autor:	Safavi, Seyede Mahya, Lopour, Beth, Chou, Pai H.
Rok vydání:	2017
Předmět:	Quantitative Biology - Neurons and Cognition
Druh dokumentu:	Working Paper
Popis:	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 $80\%$. Comment: I just don't think this version of my draft is ready
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.03536 Zobrazit plný text záznamu View this record from Arxiv