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

TL;DR

This paper introduces two techniques using dictionary learning and electrode exclusion to significantly reduce the computational complexity of EEG source localization with the MUSIC algorithm, maintaining accuracy.

Contribution

The paper proposes novel dictionary learning and electrode exclusion methods to reduce MUSIC algorithm's computational load while preserving localization accuracy.

Findings

Computational complexity reduced by up to 80%.

Cramer-Rao bound of localization maintained.

Effective cortex surface parsing for faster localization.

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 $80\%$.