对干净的EEG应用独立分量分析（ICA）的电位平均叠加技术提取的视觉诱发脑电（VEP）
特别是独立分量分析（ICA）算法中经典的FastICA算法分析进行详细分析
并对采集的脑电信号（EEG）进行FastICA算法消噪、提取
BSS）技术多维信号处理方法的独立分量分析（ICA）算法介绍
分析VEP信号中的相关事件电位P3
） Abstract： Through blind source separation (Blind Source Separation, BSS) technology multi-dimensional signal processing method of independent component analysis (ICA) algorithm, in particular the independent component analysis (ICA) algorithm in the classical FastICA algorithm to conduct a detailed analysis of the analysis, the use of this The 24 lead EEG laboratory data acquisition equipment on the outside noise interference cases, a colleague of the EEG (EEG) for the collection, and collection of the EEG (EEG) conducted FastICA algorithm noise, extraction, reduced Clean EEG, to form an independent component of the topographic maps of brain, eyes and electricity, power-frequency spectrum interference, on that basis, the application of clean EEG Independent component analysis (ICA) of the average potential stacking technology of extraction of visual evoked EEG ( VEP), analysis of the VEP signal of potential events related to P3, the P3 on the sub-components of that analysis, and reconstruction
These sub-component will help us to human cognition, such as high-level neural activity for more in-depth research to help people in the field of cognitive further exploration
Keywords： EEG;ICA;FastICA;P3;VEP 下载PDF阅读器 PDF全文下载： 初稿 （ 200 ） 作者简介： 通信联系人： 【收录情况】 中国科技论文在线： 王永飞
基于ICA在脑电信号消噪和P3亚成分提取研究[EB/OL]
北京：中国科技论文在线
利用本实验室的24导脑电数据采集仪器对有外噪音干扰情况下一位同事的脑电信号（EEG）进行采集
