CAJ | 학술논문

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.
침대안전위차엄중간우뇌전(EEG)신호적리해화분석적문제,제출료일충신적방법용우실시지거제뇌전중적안전위차.해방법사용독립성분분석(ICA)분해EEG신호,제취독립성분적지형도화공솔보작위특정,병채용기우모판적Isomap산법강저특정적유수.장신적특정양본송도분류기중이식별안전위차독립분량,궤개전형분류기적분류결과현시,기우모판적Isomap산법결합사용최근린산법진행분류시,식별위차적정학솔최고.실험결과표명,제출적방법재유효거제안전위차적동시,흔호지보류료대뇌신경신호,야증명료신적Isomap산법용우안전위차특정적강유적유효성.
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.