期刊
INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY
卷 53, 期 2, 页码 105-119出版社
ELSEVIER
DOI: 10.1016/j.ijpsycho.2004.03.007
关键词
electroencephalography; ocular artifact; regression; principal components analysis; independent components analysis; adaptive filter
资金
- NIMH NIH HHS [MH18951, MH30915, MH56193] Funding Source: Medline
A variety of procedures have been proposed to correct ocular artifacts in the electroencephalogram (EEG), including methods based on regression, principal components analysis (PCA) and independent component analysis (ICA). The current study compared these three methods, and it evaluated a modified regression approach using Bayesian adaptive regression splines to filter the electrooculogram (EOG) before computing correction factors. We applied each artifact correction procedure to real and simulated EEG data of varying epoch lengths and then quantified the impact of correction on spectral parameters of the EEG. We found that the adaptive filter improved regression-based artifact correction. An automated PCA method effectively reduced ocular artifacts and resulted in minimal spectral distortion, whereas ICA correction appeared to distort power between 5 and 20 Hz. In general,, reducing the epoch length improved the accuracy of estimating spectral power in the alpha (7.5 - 12.5 Hz) and beta (12.5 - 19.5 Hz) bands, but it worsened the accuracy for power in the theta (3.5 - 7.5 Hz) band and distorted time domain features. Results supported the use of regression-based and PCA-based ocular artifact correction and suggested a need for further studies examining possible spectral distortion from ICA-based correction procedures. (C) 2004 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据