工件(错误)
计算机科学
脑电图
维纳滤波器
算法
滤波器(信号处理)
转化(遗传学)
人工智能
模式识别(心理学)
频道(广播)
计算机视觉
生物化学
基因
精神科
化学
计算机网络
心理学
作者
Ben Somers,Tom Francart,Alexander Bertrand
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2018-02-27
卷期号:15 (3): 036007-036007
被引量:180
标识
DOI:10.1088/1741-2552/aaac92
摘要
The electroencephalogram (EEG) is an essential neuro-monitoring tool for both clinical and research purposes, but is susceptible to a wide variety of undesired artifacts. Removal of these artifacts is often done using blind source separation techniques, relying on a purely data-driven transformation, which may sometimes fail to sufficiently isolate artifacts in only one or a few components. Furthermore, some algorithms perform well for specific artifacts, but not for others. In this paper, we aim to develop a generic EEG artifact removal algorithm, which allows the user to annotate a few artifact segments in the EEG recordings to inform the algorithm.We propose an algorithm based on the multi-channel Wiener filter (MWF), in which the artifact covariance matrix is replaced by a low-rank approximation based on the generalized eigenvalue decomposition. The algorithm is validated using both hybrid and real EEG data, and is compared to other algorithms frequently used for artifact removal.The MWF-based algorithm successfully removes a wide variety of artifacts with better performance than current state-of-the-art methods.Current EEG artifact removal techniques often have limited applicability due to their specificity to one kind of artifact, their complexity, or simply because they are too 'blind'. This paper demonstrates a fast, robust and generic algorithm for removal of EEG artifacts of various types, i.e. those that were annotated as unwanted by the user.
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