title: Consistent sparse representations of EEG ERP and ICA components based on wavelet and chirplet dictionaries.
abstract: 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.
authors.affiliation: Computer Science Department of the National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan, R.O.C.
authors.forename: Jun-Wei
authors.initials: JW
authors.lastname: Qiu
authors.forename: John K
authors.initials: JK
authors.lastname: Zao
authors.forename: Peng-Hua
authors.initials: PH
authors.lastname: Wang
authors.forename: Yu-Hsiang
authors.initials: YH
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D005071:Evoked Potentials; D006801:Humans