期刊
NEUROCOMPUTING
卷 103, 期 -, 页码 222-231出版社
ELSEVIER
DOI: 10.1016/j.neucom.2012.09.024
关键词
EEG; Artifact; Adaptive FLN-RBFN-based filter; Adaptive noise cancellation
EEG signal is an important clinical tool for diagnosing, monitoring, and managing neurological disorders. This signal is often affected by a variety of large signal contaminations or artifacts, which reduce its clinical usefulness. In this paper, a new adaptive FLN-RBFN-based filter is proposed to cancel the three most serious contaminants, i.e. ocular, muscular and cardiac artifacts from EEG signal. The basic method used in this paper for the elimination of artifacts is adaptive noise cancellation (ANC). The results demonstrate the effectiveness of the proposed technique in extracting the desired EEG component from contaminated EEG signal. (C) 2012 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