一种使用小波变换和独立成分分析从脑电图数据中抑制眼电伪迹的全自动方法。
A fully automatic method for ocular artifact suppression from EEG data using wavelet transform and independent component analysis.
作者信息
Ghandeharion Hosna, Erfanian Abbas
机构信息
Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran.
出版信息
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:5265-8. doi: 10.1109/IEMBS.2006.259609.
Contamination of electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent component analysis (ICA) is a general accepted tool for isolating artifactual components. One major challenge to artifact removal using ICA is the automatic identification of the artifactual components. However there is still little consensus on criteria for automatic rejection of undesired components. In this paper we present a new identification procedure based on an efficient combination of statistical and wavelet-based measures for ocular artifact suppression. The results on 420 4-s EEG epochs indicate that the artifact components can be identified correctly with 96.4%
脑电图（EEG）记录被不同类型的伪迹污染是EEG数据分析的主要障碍。独立成分分析（ICA）是一种普遍认可的用于分离伪迹成分的工具。使用ICA去除伪迹的一个主要挑战是自动识别伪迹成分。然而，对于自动剔除不需要成分的标准仍几乎没有共识。在本文中，我们提出了一种基于统计和基于小波的测量方法有效组合的新识别程序，用于抑制眼动伪迹。对420个4秒EEG片段的结果表明，伪迹成分的正确识别率可达96.4%
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