Journal
NEUROCOMPUTING
Volume 103, Issue -, Pages 222-231Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2012.09.024
Keywords
EEG; Artifact; Adaptive FLN-RBFN-based filter; Adaptive noise cancellation
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available