西安交通大学生命科学与技术学院,西安,710049
网络首发:2010-02-10,
纸质出版:2010
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高军峰, 郑崇勋, 王沛. 基于独立成分分析和流形学习的眼电伪差去除[J]. 西安交通大学学报, 2010,44(2):113-118.
Real-Time Removal of Ocular Artifacts from EEG Signals Using ICA and Manifold Algorithm[J]. 2010, 44(2): 113-118.
针对眼电伪差严重干扰脑电(EEG)信号的理解和分析的问题
提出了一种新的方法用于实时地去除脑电中的眼电伪差.该方法使用独立成分分析(ICA)分解EEG信号
提取独立成分的地形图和功率谱作为特征
并采用基于模板的Isomap算法降低特征的维数.将新的特征样本送到分类器中以识别眼电伪差独立分量
几个典型分类器的分类结果显示
基于模板的Isomap 算法结合使用最近邻算法进行分类时
识别伪差的正确率最高.实验结果表明
提出的方法在有效去除眼电伪差的同时
很好地保留了大脑神经信号
也证明了新的Isomap 算法用于眼电伪差特征的降维的有效性.
Aiming at the problem that frequent occurrences of ocular artifacts seriously interfere with the electroencephalogram(EEG)interpretation and analysis
a novel technique to eliminate ocular artifacts from EEG signals in real-time is proposed.The independent component analysis(ICA)is employed to decompose EEG signals
and these independent components features of topography and power spectral density are extracted. Specifically
a template-based isometric mapping(Isomap)algorithm is adopted to reduce the feature dimensionality.The low-dimensional feature samples are fed to a classifier to identify ocular artifacts components. The classification performances of several typical classifiers show that the template-based Isomap algorithm with the nearest neighbor classifier performs best. The experimental results demonstrate the efficiency for removing ocular artifacts with little distortion of underlying brain signals.
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基于近似熵的认知能力对事件相关电位的影响研究,2008,42(10): 1300-1303.
基于脑电功率谱-连续隐马尔科夫链的精神疲劳分级模型,2007,41(12): 1474-1478.
基于样本熵的注意力相关脑电特征信息提取与分类,2007,41(10): 1237-1241.
基于功率谱分析的头皮电位和心率变异性关系研究,2007,41(8): 991-994.
基于多通道线性描述参数的生理性精神疲劳分析,2007,41(6): 737-740.
基于多导脑电特征的生理性精神疲劳分析,2007,41(2): 250-254.
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探测深度对心脏光学标测中荧光信号的影响,2006,40(10): 1157-1160.
一种新的注意力相关脑电分类算法设计,2005,39(10): 1162-1164.
基于多通道脑电特征运动意识任务的分类,2005,39(8): 904-907.
基于非线性参数的意识任务分类,2005,39(8): 900-903.
实时脑电信号眼电伪差去除方法的研究,2004,38(12): 1306-1309.
利用双谱分析的癫痫脑电特征研究,2004,38(10): 1097-1100.
快速多变量自回归模型的意识任务的特征提取与分类,2003,37(8): 861-864.
基于多通道时频相干的诱发电位单次提取,2003,37(6):638-641.
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