【摘要】 利用ICA可将混合在观测信号中的相互独立的源信号分离出来的特性,针对脑电信号及其事件相关电位(ERP)的特点,提出一种基于ICA的ERP快速提取算法,并应用于仿真数据分离和实际脑电信号ERP提取.实验结果表明,该算法具有较强的稳健性和实用性.更多还原

【Abstract】 Independent component analysis (ICA) is a novel blind source separating method, which can extract individual signals from mixtures of signals. According to the peculiarities of electroencephalogram (EEG) and event related potentials (ERP), a fast algorithm based on separating characters of ICA is used to extract the useful information of ERP from EEG. A brief illustration of the basic principles and common criteria of ICA is first given, together with explanations of the ICA algorithm. ICA is then applied in the separation of simulated data set and extracting ERP from EEG. Satisfactory results obtained in both aspects illustrate the performance and validity of the algorithm.更多还原