Detection and classification of subject-generated artifacts in EEG signals using autoregressive models
Detection and classification of subject-generated artifacts in EEG signals using autoregressive models
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DOI：
10.1016/j.jneumeth.2012.05.017
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发表时间：
2012-07-15
影响因子：
3
通讯作者：
Robbins, Kay
中科院分区：
文献类型：
--
作者：
Lawhern, Vernon;Hairston, W. David;Robbins, Kay
我们研究了连续EEG记录中伪影的准确检测和分类问题。通过专家或专家小组手动识别工件对于大型数据集来说可能是繁琐、耗时且不可行的。我们使用自回归（AR）模型的特征提取和表征的EEG信号包含几种主体产生的文物。AR模型参数是尺度不变的特征，可用于开发人群中的伪影模型。我们使用支持向量机（SVM）分类器，以区分使用AR模型参数作为特征的伪影条件。结果表明，在几个不同的伪影条件之间的受试者（约94%）的可靠分类。这些结果表明，AR建模可以是一个有用的工具，用于区分工件信号内和跨个人。(C)2012爱思唯尔有限公司版权所有。
We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals. (C) 2012 Elsevier B.V. All rights reserved.