標題: Consistent Sparse Representations of EEG ERP and ICA Components Based on Wavelet and Chirplet Dictionaries
作者: Qiu, Jun-Wei
Zao, John K.
Wang, Peng-Hua
Chou, Yu-Hsiang
資訊工程學系
Department of Computer Science
公開日期: 2010
摘要: a randomized search algorithm for sparse representations of EEG event-related potentials (ERPs) and their statistically independent components is presented. This algorithm combines greedy matching pursuit (MP) technique with covariance matrix adaptation evolution strategy (CMA-ES) to select small number of signal atoms from over-complete wavelet and chirplet dictionaries that offer best approximations of quasi-sparse ERP signals. During the search process, adaptive pruning of signal parameters was used to eliminate redundant or degenerative atoms. As a result, the CMA-ES/MP algorithm is capable of producing accurate efficient and consistent sparse representations of ERP signals and their ICA components. This paper explains the working principles of the algorithm and presents the preliminary results of its use.
URI: http://hdl.handle.net/11536/25565
ISBN: 978-1-4244-4124-2
ISSN: 1557-170X
期刊: 2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
起始頁: 4014
結束頁: 4019
Appears in Collections:Conferences Paper