基于独立成分分析和流形学习的眼电伪差去除

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.

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

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 to-pography and power spectral density are extracted. Specifically, a template-based isometric map-ping (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.