A Geometrically Faithful Memetic Algorithm for Searching Sparse Representations of EEG Signals

Abstract

This paper presents a memetic algorithm, christened the natural memetic pursuit (NMP), that was designed to search for sparse signal representations. This algorithm combines global sampling based on a Grassmannian dictionary of atomic signals with local search using natural gradient decent in the signal parameter space. Performance of the algorithm was demonstrated by analyzing Jung and Makig's ERP data sets. It can obtain unbiased sparse representations of EEG signals in far less iteration than its predecessor, the stochastic matching pursuit (SMP).