Sparse algorithms for EEG source localization

TL;DR

This paper reviews sparse algorithms for EEG source localization, implements the CARSS method, and compares its performance across various simulated and real EEG data scenarios.

Contribution

It provides a comprehensive review of sparse EEG source localization methods and evaluates the CARSS algorithm through extensive simulations and real data analysis.

Findings

CARSS performs well in source localization accuracy

Sparse methods improve localization with fewer sensors

Performance varies with noise levels and source complexity

Abstract

Source localization using EEG is important in diagnosing various physiological and psychiatric diseases related to the brain. The high temporal resolution of EEG helps medical professionals assess the internal physiology of the brain in a more informative way. The internal sources are obtained from EEG by an inversion process. The number of sources in the brain outnumbers the number of measurements. In this article, a comprehensive review of the state of the art sparse source localization methods in this field is presented. A recently developed method, certainty based reduced sparse solution (CARSS), is implemented and is examined. A vast comparative study is performed using a sixty four channel setup involving two source spaces. The first source space has 5004 sources and the other has 2004 sources. Four test cases with one, three, five, and seven simulated active sources are considered. Two noise levels are also being added to the noiseless data. The CARSS is also evaluated. The results are examined. A real EEG study is also attempted.