標題: A Geometrically Faithful Memetic Algorithm for Searching Sparse Representations of EEG Signals
作者: Qiu, Jun-Wei
Zao, John K.
Chou, Yu-Hsiang
資訊工程學系
Department of Computer Science
關鍵字: Memetic Algorithm;Natural Gradient;Grassmannian Gabor Dictionaries;Sparse Signal Representation;EEG Signal Processing
公開日期: 2012
摘要: 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).
URI: http://hdl.handle.net/11536/20974
ISBN: 978-1-4673-1509-8
期刊: 2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
顯示於類別:會議論文