摘要
事件相关电位的特征提取分析在大脑认知的神经生理基础和临床应用研究中起着非常重要的作用。独立分量分析(Independent Component Analysis,ICA)作为目前比较流行的盲源信号处理算法,其特性非常适合应用于事件相关电位的提取。文章主要讨论了独立分量分析的基本原理、判决条件和算法,针对快速定点算法(FastIn-dependent Component Analysisalgorithm,Fast ICA)迭代次数较多和对初始权值敏感的缺点,我们引入利用梯度法改进的修正因子,在此基础上对Fast ICA进行优化,得到改进算法,改进算法降低了对初始权值的敏感性,减少了迭代次数,从而提高了算法的收敛速度。最后将其应用于ERP信号的提取当中,实验表明,在分离效果相当的前提下,收敛速度得到了较大的提高。
Feature extraction of Event-related potentials （ERP） plays an important part in both basic and clinical researches for cerebral neurophysiology. ICA is a method for separating blind signals based on signal statistic charac- teristics. In this paper, the fundamental principle, the discrimination condition and the practical algorithm of Independent Component Analysis are discussed. Then, a fast Independent Component Analysis algorithm （Fast ICA） is introduced. But like Fast ICA, its convergence is dependent on initial weight. We bring in a revision factor into the algorithm; thus the new algorithm could implement convergence on a largescale. In this paper, the revision/actor is calculated by gradient. By modifying kernel iterate course, several iterations of Fast ICA are merged into one iteration of Modified Fast ICA, so the convergence of ICA will be accelerated. Finally, Modified ICA is applied to ERP extraction. The simulation shows that the convergence speed can be increased by using the improved algorithm.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2009年第4期766-770,共5页
Journal of Biomedical Engineering
关键词
独立分量分析
事件相关电位
FAST
ICA
脑电
盲源分离
Independent Component
Analysis（ICA）
Event-related potentials（ERP）
Fast ICA
EEG
Blind Source Separation（BSS）
