Real-Time Removal of Ocular Artifacts from EEG Signals Using ICA and Manifold Algorithm

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Real-Time Removal of Ocular Artifacts from EEG Signals Using ICA and Manifold Algorithm

更新时间：2025-07-09

- Real-Time Removal of Ocular Artifacts from EEG Signals Using ICA and Manifold Algorithm
- Vol. 44, Issue 2, Pages: 113-118(2010)
- 作者机构：
  
  西安交通大学生命科学与技术学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： R318
- Online First：10 February 2010，
  
  Published：2010
- 稿件说明：
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Real-Time Removal of Ocular Artifacts from EEG Signals Using ICA and Manifold Algorithm[J]. 2010, 44(2): 113-118.
DOI：

Real-Time Removal of Ocular Artifacts from EEG Signals Using ICA and Manifold Algorithm[J]. 2010, 44(2): 113-118. DOI：

摘要

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.

Abstract

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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references

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