Qiu Jun-Wei, Zao John K, Wang Peng-Hua, Chou Yu-Hsiang
Computer Science Department of the National Chiao Tung University, 1001 University Road, Hsinchu 300, Taiwan, R.O.C.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4014-9. doi: 10.1109/IEMBS.2010.5627995.
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.
提出了一种用于脑电图事件相关电位（ERP）及其统计独立成分稀疏表示的随机搜索算法。该算法将贪婪匹配追踪（MP）技术与协方差矩阵自适应进化策略（CMA-ES）相结合，从超完备小波和chirplet字典中选择少量信号原子，以提供对准稀疏ERP信号的最佳近似。在搜索过程中，使用信号参数的自适应修剪来消除冗余或退化原子。结果，CMA-ES/MP算法能够生成准确、高效且一致的ERP信号及其独立成分分析（ICA）成分的稀疏表示。本文解释了该算法的工作原理，并展示了其使用的初步结果。
